Abstract:In order to determine the optimal contact parameter combination of discrete element simulation of coated corn seeds, the error rates of repose angle and stacking angle of coated corn seeds obtained from the real test and simulation under different parameter combinations were used as response values to calibrate the discrete element simulation parameters of coated corn seeds. The classical mechanics theory was used to establish the kinetic equations of granular particles, and the main contact parameters were determined by the analytical mechanics equations. The Central Composite test was used to establish the multiple quadratic regression equation between the model parameters and the response values. The genetic algorithm NSCA-Ⅱ was used to optimize the multiple quadratic equation, and the verification test was carried out to obtain the optimal contact parameter combination of the coated corn seed discrete element model, that was the interspecies static friction coefficient was 0.432, the interspecies rolling friction coefficient was 0.082, and the interspecies collision recovery coefficient was 0.178. Combined with bench test and simulation test, through inclined plane sliding test, the static friction coefficient between horse tooth corn seed and plexiglass was 0.1164. In order to verify the accuracy of the calibration parameters, the four contact parameters were used to carry out the simulation test of seed ejection, the simulation test result of the stacking angle obtained by the test was 27.83°, compared with the actual values of seed dropping, the error was 1.76%, the results showed that the demarcated discrete element model and contact parameters of coated maize seeds were accurate and reliable, and could be used in discrete element simulation experiments.

Abstract:In China, the intelligent level of corn harvesters is low, and the parameters of corn harvesters are mostly adjusted trough mechanical levers, the moisture content of corn is high during harvest in most areas, and the corn broken rate and entrainment loss rate of high moisture content corn are high. Thus, a longitudinal axial flow corn harvester was refit intelligently, and an intelligent control system for corn threshing with high moisture content and low loss based on CAN-bus was designed. According to the need of working process of corn harvester, two control modes, manual control and automatic control, were designed, the automatic control strategy was designed，and the discrete PID control algorithm was used to realize the low loss threshing of high moisture content corn. The control strategy test and field experiment of the system were carried out, the control strategy test showed that the working parameters can be adjusted according to the control strategy and were stable in the set threshold value, the field experiment showed that the system can make the broken rate and the loss rate of entrainment meet the national standard, Among them, about the broken rate of corn, the lowest was 3.35%, the highest was 4.05%, and the average was 3.75%; about the entrainment loss rate of corn, the lowest was 1.56%, the highest was 2.08%, and the average was 1.77%.

Abstract:In view of the phenomenon of deformation, fracture and damage at the joint of shovel handle and frame of orchard subsoiler in China, a chisel-type shovel subsoiler was designed. CATIA was used to build the 3D model of the chisel-type shovel subsoiler. ABAQUS, ISIGHT and FE-SAFE software were used to optimize the structure and analyze the fatigue life of the digital model. Field test results showed that when the lightweight subsoiler was operating, the forward speed was 2.1km/h, the tillage depth was 350mm, and the soil moisture content was 15%, the smaller the strength performance torque was, the best subsoil effect can be got. After the optimization design of the software ISIGHT and the fatigue life analysis of FE-SAFE, the mass of the whole machine was reduced from 0.55t to 0.45t, and the mass of the whole machine was reduced by 16.4%, realizing a lightweight design, the research result can provide a certain reference for the independent research and development of orchard subsoilers.

Abstract:In view of tobacco leaf loading and unloading with labor-intensive, high cost, potential safety hazards and other issues, double-rotating tobacco leaf loading and unloading device for the tobacco loading machine in the curing barn was designed, including tobacco carrier, double rotating mechanism, double rotating mechanism and PLC control system. Herein, a main and auxiliary rotating machine composed of bevel gear sets was proposed, it can solve the problems of falling off, scratching and interference in the process of tobacco leaf loading and unloading. Through using projection method and genetic algorithm to design mechanism, the structural parameters was determined. In order to ensure that the machine had good loading and unloading effect, the space force system and kinematics of the smoke-carrying mechanism and the tilting mechanism were analyzed, and their structural parameters were determined. Taking the inclination angle of the cigarette holder, the rotating speed of the hollow shaft, and the length of the tobacco rod as the test factors, and the success rate of tobacco leaf loading and unloading, the orthogonal test was carried out. The test results showed that when the tilt angle of the cigarette holder was 23°, the rotating speed of the hollow shaft was 12r/min, and the length of the tobacco rod was 650mm, the loading and unloading effect of the cigarette holder was the best. At this time, the tobacco leaf shedding rate was 1.5%, the excursion rate of the cigarette holder was 1.4%, the damage rate of tobacco leaves was 2.1%, the success rate of tobacco leaf loading and unloading was 95%, and the device had high reliability.

Abstract:In order to detect the fog droplet parameters related to the unmanned aerial vehicle (UAV) spray quality and its application in a variety of complex environments, a spray quality testing system for UAV based on waterborne acrylic resin and digital image processing technology was designed. The system consisted of droplet sampling module, image acquisition module, image processing module, wireless communication module, image and data storage module, and data display module. Among them, the water-based acrylic resin would have discoloration reaction when it met water, and it was made into a fog drop sampling module, and the fog drop image of the fog drop collection device was obtained, and then the digital image processing technology was used to process the fog drop image, and the fog drop parameters were calculated. The performance of the system was evaluated by UAV spray test in farmland environment. The system can detect two droplet sizes, droplet deposition density and droplet coverage rate in real time, so as to realize the detection of UAV spray quality. The preliminary experimental results showed that the system ran stably, and the distribution curve of detection results of the system was consistent with that of the water sensitive paper method on the whole. And it can timely and accurately detect the quality of UAV spray. The research results can provide data support for the further development of UAV automatic spray system and aviation drug application decision system.

Abstract:Plant height and stem thickness are important phenotypic parameters in the plant type of maize, which can reflect the growth status and vigor of maize. Terrestrial laser scanning can realize the rapid and automatic measurement of phenotypic parameters. Firstly, the three-dimensional point cloud data of two varieties of maize in four growth periods were obtained by using terrestrial laser scanning. Secondly, the point cloud processing software was used to match and segment the collected maize point cloud data, artificial measurement of maize phenotype in point cloud. Then, the maize point cloud was processed by down sampling, through filtering, plane segmentation algorithm based on random sampling consistency, statistical filtering and cylinder segmentation. The results showed that the method can segment individual maize, remove ground point cloud, filter outliers, layer plant point cloud and extract stem point cloud to be measured. The highest point extraction and ground point segmentation were used to measure maize plant height, and the long axis and short axis of stem thickness were measured by ellipse fitting. Compared the artificial point cloud measurement value with the artificial field measurement value, the root mean square error (RMSE) of Jingnongke 728 plant height, long axis and short axis were 2.15cm, 1.24mm and 1.86mm, respectively. The RMSE of Nongda 84 plant height, long axis and short axis were 2.36cm, 1.56mm and 1.23mm, respectively. Compared the automatic point cloud measurement value with the artificial point cloud measurement value, the RMSE of Jingnongke 728 plant height, long axis and short axis were 1.02cm, 6.65mm and 3.45mm, respectively. The RMSE of Nongda 84 plant height, long axis and short axis were 0.71cm, 4.95mm and 3.26mm, respectively. The results showed that the method of measuring plant height and stem thickness with terrestrial laser scanning point cloud data can be widely used in different maize varieties with different growth periods. The result of this method was highly consistent with that of the artificial measurement method and it can replace the artificial measurement method. It can provide a fast, automatic and accurate measurement scheme for breeders and crop phenotypists.

Abstract:In order to improve the speed and accuracy of terrain measurement in farmland leveling operation, a method of farmland leveling terrain measurement was proposed based on UAV LiDAR. Firstly, high-precision farmland terrain point cloud data was obtained by using LiDAR measurement system equipped with UAV. After preprocessing the collected point cloud data, a large number of noise points along the z-axis were removed by direct filtering. Then, the progressive morphological filtering algorithm was used to segment farmland surface points and non surface points. Finally, on the basis of retaining the main farmland terrain information features, the farmland terrain point cloud reduction method based on VoxelGrid filter was used to simplify the farmland terrain point cloud data. The results showed that compared with the flatness of GNSS, the accuracy was 94.681%, 91.364%, 90.588% and 90.287% respectively; compared with the maximum elevation difference of GNSS, the accuracy was 99.391%, 98.167%, 97.025% and 98.776% respectively. Compared with the height difference distribution of GNSS, the accuracy was 99.307%, 97.914%, 98.673% and 95.110% respectively. The farmland terrain measurement method proposed based on UAV LiDAR can quickly and accurately measure the farmland terrain information, and provide data support for the next farmland leveling operation.

Abstract:In order to realize the obstacle avoidance of automatic navigation agricultural machinery and solve the problem that the panoramic camera mounted on the top of the agricultural machinery needs to accurately and quickly detect obstacles in real time to obtain the 360° image information around it, an improved YOLO v3-tiny target detection model was proposed, which can realize the detection and identification of pedestrians and other agricultural machinery in the field. In order to improve the detection effect of small targets in panoramic images, the fast detection speed and lightweight network model YOLO v3-tiny was used as the basic framework, and the splicing layer before the second YOLO prediction layer was used as the third prediction layer by fusing the shallow features with the second YOLO prediction layer to increase the detection effect of small targets; in order to further increase the network model's ability to extract target features, borrowing the idea of residual network, the residual module was introduced on the YOLO v3-tiny backbone network to increase the depth and learning ability of the network, so that it can better improve the detection capabilities of the network. In order to verify the performance of the model, totally 1100 original data sets of pedestrian and agricultural machinery obstacles in the farmland environment were established, after data amplification, totally 2200 images data sets were obtained, the data sets were divided into training set, verification and test set according to 8∶1∶1, and the model was trained under the Pytorch 1.8 deep learning framework. After the model was trained, totally 220 images of test set were used to test different models. The test results showed that the farmland obstacle detection model based on improved YOLO v3-tiny had an average accuracy rate and recall rate of 95.5% and 93.7%, respectively, which were 5.6 percentage points and 5.2 percentage points higher than that of the original network model. Single panoramic image detection took 6.3ms, the average frame rate of video stream detection was 84.2f/s, and the model memory was 64MB. The improved model can meet the real-time obstacle detection requirements of agricultural machinery in motion while ensuring high detection accuracy.

Abstract:In order to realize the positioning and quantitative fertilization of maize in seedling stage under farmland environment, a control system was designed, which was based on machine vision to position and fertilize maize in seedling stage and improve fertilizer utilization rate. The control system controlled the maize positioning fertilizer applicator to travel in the field through the self-driving crawler. The fertilizer applicator realized intermittent fertilization according to plant location by using the structure of slice fertilizer discharger. After collecting the images of maize canopy at seedling stage, the area of maize plants at seedling stage was small, and there were many collected images without maize. In order to solve the influence of the above problems on operation efficiency and accuracy, the system used color features to distinguish a large number of background images and misrecognized plant centers during continuous shooting. By adding image preprocessing and algorithm post-processing, the method of plant center recognition and location was improved; in order to solve the problem of fertilizer lag caused by fertilizer falling time, system response time and positioning error, the compensation model of fertilization lag error was established, the relative distance of fertilization was calculated in real time, and the time of fertilizer falling was calculated accurately, which improved the control precision of intermittent automatic positioning fertilization according to plant. After experiment and verification analysis, the improved plant center location recognition method obviously reduced the image processing time and improved the robustness of the algorithm; according to the amount of single fertilization, the fertilizer applicator was set three gears (7.25g, 14.5g and 21.75g), and the coefficient of variation of fertilization stability, which represented the stability of fertilizer application per plant, was 1.93%, 1.87% and 1.93%, respectively. The control precision of the actual fertilization amount and the target fertilization amount of the fertilizer discharger was more than 95%. The average error of the distance between the actual fertilizer drop point and the target drop point can reach 3.2cm, which met the basic operation requirements of location fertilization. The fertilization control system designed can realize the quantitative fertilization according to the location of maize plants at seedling stage, and achieve the purpose of reducing application, improving quality and scientific fertilization.

Abstract:The accuracy of fertilization position is an important aspect affecting the performance of variable rate of fertilization (VRF) applicator.Aiming at the problem of position lag in VRF,on the basis of existing research, with the goal of improving the operation accuracy of VRF, a lag time detection system was built by using pressure sensor, ArduinoUNO and other equipment which could detect, show and save the lag time date, and the relationship model between fertilizer dropping time and its influence factors was established. Based on the relationship model between factors, a method of fertilizing position correction based on lag distance was proposed based on the data. This method used the lag time and the operating speed to calculate the lag distance. When the distance between the machine and the grid boundary was equal to the lag distance, the fertilization state was changed to realize the correction of the fertilization position. Based on the lag time measured in the field experiment, the lag time detected under different states (static and dynamic) and different rotating speeds of the variable rate fertilizer applicator was analyzed, and the field lag correction experiment of the variable rate fertilizer applicator was carried out. Field experiment results showed that the detection accuracy of the lag time detection method proposed was not less than 84.1%; The established speed-lag time model can accurately calculate the lag time, and using the fertilization position correction method based on the model, the fertilization position correction experiment was carried out at the same vehicle speed (4.25km/h) and different motor speeds, and the fertilization position lag distance was reduced by at least 61.8%. The method proposed can effectively improve the lag of fertilization position of VRF applicator and meet the requirements of field work.

Abstract:Aiming at the problem that the breast and belly circumference of pigs cannot be accurately measured due to the barriers of the railing in the study of pig body size detection based on depth camera, a method of combining cubic B-spline curve fitting and edge detection based on threshold analysis was proposed, to achieve the completion of the missing area of the side-view point cloud.The original point cloud data on the left side of the fake pig collected by the DK depth camera was preprocessed, and the side-view point cloud of the pig was extracted by methods such as straight-through filtering, voxel grid down-sampling, statistical filtering and Euclidean clustering. Perform slicing, threshold analysis, projection, and fitting of the side-view point cloud were carried out to obtain the complementary point cloud after curve fitting, and then edge detection was performed on the side-view point cloud to obtain the edge point cloud, and the missing part of the edge point cloud was fitted and complemented to obtain a complete edge point cloud, the complementary point cloud after curve fitting was analyzed and compared with the edge point cloud, and the complementary point cloud was removed beyond the edge to obtain the final side-view complementary point cloud.Automatic extraction and manual extraction of body size measurement points were performed on the uncompleted point cloud and the complete point cloud respectively. The experimental results showed that after completing the missing point cloud of the abdomen by the method, the selection error of the abdominal measurement point was reduced from 1.54cm to 0.64cm. After the missing point cloud of the chest was completed, the selection error of the chest measurement point was reduced from 3.41cm to 0.89cm. The research results can provide a method for the side-view point cloud completion of pigs.

Abstract:Aiming at the problem of poor stationarity of robot end motion in the process of complex shape shrub pruning, an improved cubic B-spline trajectory fairing algorithm based on double tangent vector continuity was proposed. Firstly, the transition model of linear trajectory corner was established. The tangent vector constraint of connecting point was constructed by using the local properties of B-spline curve, so that the initial unsmooth pruning trajectory can be G 2 continuous after fairing. Secondly, the transition curves of planar multi-segment trajectories under different fairing algorithms were constructed. Through comparison and analysis, under the same approximation error, the curvature extreme value of the transition curve constructed by this algorithm was 40.5% lower than that of the traditional arc transition algorithm. Compared with the traditional cubic B-spline transition algorithm, the curvature of the transition curve constructed by this algorithm was more continuous and the overall fairing effect was better than that of the traditional cubic B-spline transition algorithm under the same approximation error. Finally, in order to verify the fairing effect of the algorithm on spatial pruning trajectory, a 5-axis joint robot model was established for duck-shaped trajectory pruning simulation experiment. The results showed that the algorithm can increase the extreme velocity at the end of the robot by 13.5%, reduce the extreme acceleration by 86.9%, and make the joints of the robot move more smoothly. The results verified the feasibility and effectiveness of the algorithm in the fairing of complex pruning trajectories.

Abstract:Timely, comprehensive and accurate estimation of banana yield can provide growers with decisions on variable fertilization, irrigation, harvest planning, marketing and forward sales. To improve the accuracy of banana remote sensing yield estimation, totally 71 banana fields in Fusui County, Guangxi were used as the study area, and a remote sensing prediction model for banana yield in 2019—2020 was conducted by using time-series Sentinel-2 remote sensing image data, combined with field measured yield data. The method firstly obtained Sentinel-2 images during the key banana phenological period of 2019—2020, then the threshold segmentation and morphological open operation methods were used to remove cloud and cloud shadow coverage areas, the average normalized difference vegetation index (NDVI) values of each plot were extracted, and finally the BSO-SVR model was used to predict and evaluate the banana yield in combination with the actual measured data of banana yield. The results showed that compared with the grid search (GS) and grey wolf optimizer (GWO) algorithms to optimize the penalty factor and kernel function parameters of the SVR model, the brain storming optimization algorithm proposed had higher prediction accuracy and faster prediction speed. The running times of the BSO-SVR model in 2019 and 2020 were 0.320s and 0.331s, respectively, and for the validation set, the R 2 of the BSO-SVR model was 0.777 and 0.793 in 2019 and 2020, respectively; for the test set, the R 2 of the BSO-SVR model was 0.765 and 0.636 in 2019 and 2020, respectively, except that the R 2 of the BSO-SVR model in 2019 is slightly lower than that of the GS-SVR model (R 2=0.797) in 2019, except that the R 2 of the BSO-SVR model was higher than that of the GWO-SVR model and the GS-SVR model, and in addition, the overall performance of the RMSE and MAE of the BSO-SVR model was optimal in 2019—2020 compared with that of the GWO-SVR model and the GS-SVR model, indicating that the prediction results of the BSO-SVR model were closer to the actual values and with higher forecasting accuracy. Compared with the traditional ridge regression (RR) and partial least squares regression (PLSR) models, in 2019, the BSO-SVR model had the highest R 2, followed by the RR model, and the PLSR model was the worst, where the BSO-SVR model had R 2 above 0.75 for both the validation and test sets, which was 0.113 and 0.174 higher than that of the RR model, and 0.192 and 0.184 higher than thta of the PLSR model, respectively. Meanwhile, the BSO-SVR model had the lowest RMSE and MAE compared with the RR model and PLSR model, indicating that the BSO-SVR model had good results in forecasting banana yield in 2019. In 2020, the BSO-SVR model had the best overall performance, with the average R 2 of 0.715 for the validation and test sets, and the R 2 of the validation and test sets were higher than that of the RR model by 0.035 and 0.014, respectively, and better than that of the PLSR model by 0.040 and 0.035, while the RMSE and MAE of the BSO-SVR model also had the best overall performance. The banana time-series yield estimation model proposed achieved accurate yield prediction of banana field plots, which can provide an effective way for field-scale crop yield estimation.

Abstract:Kinect v2 depth measurement is greatly affected by the lighting environment, in order to quantitatively analyze the influence of lighting factors on Kinect v2 measurement accuracy, a Kinect v2 depth measurement test system was constructed, orthogonal tests of depth measurement at different distances and different lighting intensities were conducted, the specific areas of noise distribution in depth data were derived, a depth data correction model under the influence of lighting intensity was established. The depth measurement error was compensated. The test results showed that the noise interference in the depth data mainly existed in the edges and four corner parts, and gradually spreaded to the center part with the increase of measurement distance and light intensity, but the effect of light intensity was more significant, and the effective rate of depth data did not exceed 60% when the light intensity exceeded 6500lx. The average relative error of depth data measurement was reduced by 44.55% after the correction of depth measurement data by using the light intensity compensation model. In addition, it was applied in peanut canopy information acquisition to verify the effectiveness of the data correction model.

Abstract:China has always been a large agricultural country, and agricultural production has always occupied an important position. However, crops have caused huge losses due to the invasion of diseases and pests every year. Therefore, it is of great significance to study how to accurately identify crop diseases. At present, most of the research on crop disease recognition is based on public data sets, and most of these public data sets are single disease images with simple background, which often cannot meet the needs when applied in the real agricultural production environment. AlexNet, DenseNet121, ResNet18 and VGG16 models were used to conduct comparative experiments on the self constructed crop image dataset 2 with complex background and the dataset 1 with open simple image background. The results showed that good results were achieved on dataset 1, and the average recognition accuracy basically reached about 90%, while the recognition effect of the model on dataset 2 was generally poor. Therefore, further relevant experiments were taken. SSD target detection model was used on data set 2 to predict the disease area of crop image with complex background. The experimental results showed that the mAP value of the final model in the test set reached 83.90%. In the future, it would be continued to optimize the algorithm to achieve high recognition accuracy for disease images with complex background, and then apply the model to the online agricultural question answering platform to realize the intelligence and efficiency of the platform.

Abstract:Early blight disease is a common disease of greenhouse tomato, which seriously damages the yield and economic benefits. As affected by complex background such as soil, ground, plastic film and lots of overlapping green leaves in greenhouse, it is difficult to recognize disease from image of tomato leaf. In order to provide a solution for such problem, an innovate tomato early blight disease spot detection method of sliding window SVM (SW-SVM) was proposed. To enhance recognition accuracy and stability, color and texture features included color moment (CM), color coherence vector (CCV) and rotation invariant co-occurrence among adjacent LBPs (RIC-LBP) features were introduced, and CCR-SVM (CM+CCV+RIC-LBP+SVM) classification model were trained by using RBF-SVM with the extracted color texture feature (CCR) from the training samples. Meanwhile, for supporting small region data set and to fulfill recognize performance under complex environment, original images were divided to small region images by applying sliding window. And small region images belonged to early blight disease spot, healthy leaves and ground background were selected and divided into three catalogs as training samples. To verify feasibility of the proposed method, offline and online experiments were conducted. For offline classification performance, cross validation average recognition rate was 99.55% and recognition rate for testing data set was 96.97%, and average testing time for a single sliding window image was 0.004s. For online detection performance, the results showed that the proposed method can realize average accuracy rate for the original images with 86.39%, average detection time of single sliding windows image with 0.073s. For rotated images and pixel value adjusted image data, average accuracy rate was 88.98% and 92.59%, respectively; average error recognition rate was 12.71% and 16.44%, respectively; average missing recognition rate was 10.93% and 7.41%, respectively; and average disease detection time of single sliding window image was 0.075s and 0.074s, respectively. As a conclusion, the offline and online experiments results showed that the proposed method of CCR-SVM realized high accuracy and low memory requirement, which could provide real-time solution for tomato early blight detection in greenhouse.

Abstract:In order to solve the problem that it is difficult to detect the early bruises of apple surface, an early bruises detection method of apple surface based on near infrared camera imaging technology and image threshold segmentation method was proposed. The near infrared image of apple samples was collected by T2SL near infrared camera, which had absolute advantages in the near infrared band imaging compared with other types of camera. The background of the near infrared image was segmented by Otsu method. The threshold of sound and bruise region segmentation was set based on the gray histogram of the image, and the bruise region of apple samples was extracted by morphological processing. The accuracy of the method was 88% for the sound samples, 90% for the samples after bruise, and 96% for the samples after 30 minutes of bruise. The early bruises detection of apple surface based on near infrared camera imaging and threshold segmentation did not need modeling learning, which was similar to an unsupervised discriminant analysis method. The results showed that the method was feasible for early bruises detection of apple surface, it can not only detect the early bruises of apple surface, but also can directly outline the location of the surface bruises, which can provide a fast and efficient method for real-time online detection of apple surface bruises.

Abstract:In order to realize the nondestructive detection of apple lesions, an apple lesions detection system based on near-infrared steady-state spatial resolution technology was designed. The system used 830nm semiconductor laser as the light source, and FDS1010 photoelectric detector with large sensitive area and sensitive sensitivity coefficient was selected to realize the nondestructive detection of internal lesions in apple. An experimental platform was built to analyze the performance of the detection system, including current stability, temperature stability and light intensity stability. The system stability met the experimental requirements. Choosing Evans Blue solid preparation of different mass fraction of pure absorption solution, the accuracy and sensitivity of detection device were validated, it was found that with the increase of solution concentration, the absorbance showed linear change trends, and in the pure absorption system in the solution it showed stable detection performance, and with different mass fraction also showed a good resolution performance. According to the principle of spatial resolution detection technology, apple lesions replacing experiment was designed, by changing the position of the light source, and recording the distance between the source-agent, on the basis of the diffuse transmission equation of the optical parameters of apple lesion location, it was consistent with the known apple internal tissue optical parameters, the results showed that the system can realize the nondestructive detection of apple internal lesions.

Abstract:Improving the technical efficiency of agricultural production is an important part to promote the high-quality development of agriculture. However, in practical application, there exist some flaws in the traditional technical efficiency evaluation model based on the frontier, such as slow computing speed and low flexibility, which make it difficult to evaluate the efficiency of a large number of new samples. For the above reasons, a method for evaluating and predicting the technical efficiency of agricultural production was proposed, which combined the DEA technical efficiency measurement model based on the frontier with the ensemble learning model, and the grape production technical efficiency dataset was used to verify the effect of the model. Experiments showed that the Stacking fusion model reached the accuracy and AUC of 94.8% and 0.984 respectively, with promising result that surpassed the other comparison models, indicating that the Stacking ensemble learning model had high accuracy, robustness and generalization ability, and can achieve more efficient, fast and stable technical efficiency evaluation.

Abstract:Aiming at the problems of huge agricultural data, low utilization rate, complex structure and fragmented knowledge in China, a top-down and bottom-up agricultural knowledge map construction method was proposed. Focusing on the four elements of crop varieties, crop diseases and insect pests, crop introduction, and model methods, the model layer was constructed from the top down, and the conceptual framework of the knowledge graph was formed through ontology modeling, the data layer was constructed from the bottom up, through data acquisition, knowledge extraction, and fusion, storing and establishing the relationship between entities. Aiming at the problem of ambiguous fields in the corpus, this method collects large number of proprietary vocabularies in the construction of knowledge graphs to segment and mark them. In order to solve the problem of multi-word in agricultural knowledge, many main crop aliases were collected and assigned as entities. Bi-LSTM-CRF was used for named entity recognition, and LSTM was used to classify the problem, and TF-IDF was used for keyword extraction, and finally the knowledge was stored in the Neo4j graph database. The research can be used for agricultural knowledge intelligent retrieval systems, intelligent search systems and other applications to improve user experience.

Abstract:At present, the popular agricultural information services are mainly telephone direct consultation, centralized technical training and expert on-site guidance in China. Due to the limitation of time and space and manpower, there is a lack of timeliness and convenience. Through the research and development of Android agricultural technology intelligent question answering robot APP, agricultural information service can be provided for farmers. Crawlers were used to collect a large number of agricultural technology Q&A data on Internet platforms, which were preprocessed to form a corpus. The CRF model was trained to recognize the agricultural technology named entity after automatically labeling the corpus features. According to word frequency and information entropy, the evaluation index of named entity was calculated to construct the triple knowledge base of “crops, pests and pesticides”. The knowledge base was imported into Neo4j to establish the agricultural technology knowledge map. The algorithm of named entity recognition and knowledge map query recommendation was integrated in Android to solve the problem of keyword recognition and query result recommendation. This question answering system can provide a intelligent solution for agricultural technology Q&A, which had a high degree of automation and application value.

Abstract:In order to solve the thorny problems in the process of classification of agricultural diseases and insect pests questions, such as fewer public data sets, shorter texts and sparse features, and difficult to learn implicit semantic information, using the hot agricultural investment network as the data source, a data set for the classification of agricultural pests and diseases was constructed, and a deep learning model BERT_Stacked LSTM for the classification of agricultural pests and diseases was proposed. Firstly, the BERT obtained the character-level semantic information of each question, and generated a hidden vector containing sentence-level feature information. Then, stacked long short-term memory network (Stacked LSTM) structure was used to learn the hidden complex semantic information. Experimental results showed the effectiveness of the proposed model. Compared with other comparative models, the model proposed had more advantages in classifying agricultural diseases and insect pests questions. The F1 score reached 95.76%, and it was widely used in public. Tested on the domain data set, the F1 score reached 98.44%, indicating that the generalization of the model was also very good.

Abstract:Diseases and pests articles identification is an important pre-task of natural language processing in the field of diseases and pests. It is of great significance to develop a fast and accurate method for diseases and pests articles identification. In order to solve the problems of insufficient learning of semantic features and insufficient use of context information in the process of diseases and pests articles identification, a neural network model of attention pooling based bi-directional long-short term memory (AP-LSTM) was proposed, which was based on attention pooling strategy and bi-directional long-short term memory (BiLSTM). The model adopted the stacked LSTM structure, which improved the learning ability of semantic features. In the stacking operation, the input vector and output vector were concatenated to further enhance the representation of semantic information. Then, a pooling strategy based on the attention mechanism was used to assign different weights to different words, so that the model can make full use of context information while grasping the keywords. The experiments were carried out on a self annotated dataset with 2500 labeled samples, including 1439 positive cases and 1061 negative cases. The precision, recall, F1 score, and accuracy of the proposed AP-LSTM model on the dataset were 92.67%, 97.20%, 94.88%, and 94.00%, respectively. The experimental results showed that the proposed AP-LSTM model can effectively identify pest literature.

Abstract:As a flexible and efficient carrier of agricultural environmental information and crop growth information acquisition technology, UAVs have been widely used in agricultural production and scientific research in recent years. UAVs equipped with perceptual imaging equipment have become an important technical means for information acquisition in smart agriculture. Together with ground or underground sensors, they form an integrated air-ground system to provide data support and decision-making basis for intelligent agricultural management. Multi-source information fusion is one of the key technologies to improve the perception ability of UAVs, and its research is of great significance to the application of UAVs. Compared with the acquisition of single information, based on the method of multi-source data fusion, various types of multi-source information are subjected to various operations and processing to extract the characteristic information of the target for analysis and understanding, and finally realize the identification and recognition of the target. The representative related research and solutions for more than 20 years at home and abroad were summarized. Starting from the characteristics of complex background of UAV images, small target, large field of view, and target rotation, recent research on UAV target detection was inducted and analyzed. Finally, the existing problems were discussed, and the future research trend and development direction judgment were given.

Abstract:In order to understand the temporal and spatial characteristics of the protection of geographical indications of poultry in China, based on the data of geographical indications of poultry products from 2002 to 2020, with the help of spatial analysis methods, the spatial distribution of geographical indications of poultry in China was analyzed from three levels: overall, department and variety. The Moran index was used to conduct spatial autocorrelation analysis of poultry geographical indication products from both global and local dimensions. The spatial distribution results showed that the overall registration of poultry geographical indications was increased, with a “strong south and weak north” distribution in the north-south direction, and a “U-shaped” distribution in the east-west direction, with obvious differences in regional distribution; poultry between different departments There were obvious significant differences in the growth trend of poultry geographical indication registration between different departments. The distribution of provinces and cities showed a “high number of provinces and cities but few”. The spatial distribution was “strong in the south and weak in the north” in the north-south direction, with obvious differences in the east-west direction; registration of geographical indications of different breeds of poultry. The quantity difference was obvious. The proportion of resources of protected varieties was not proportional to the total proportion of protected varieties. The distribution of provinces and cities was “high number of provinces and cities, and more provinces and cities without application”, and the spatial distribution was “strong in the south and weak in the north. The east was strong and the west was weak.” The spatial autocorrelation results showed that in terms of global autocorrelation, the poultry geographical indications of the departmental integration, the Ministry of Agriculture and Rural Affairs and the Trademark Office had positive spatial correlations, and the IP Offices poultry geographical indications did not exist. In terms of breeds, there was a positive correlation between the geographical indications of chicken and goose, and there was no correlation between the geographical indications of duck and pigeon. The results of partial autocorrelation showed that the protection of geographical indications in most provinces in the provinces that registered geographical indications of poultry would affect the protection of geographical indications of poultry in neighboring provinces. In terms of aggregation differences, the overall and inter-departmental aggregation distribution of poultry geographical indications was similar. The provinces and cities with high-high aggregation were concentrated in the southern coastal area, low-low aggregation in the northern region, and low-high aggregation in the central and southern regions. High-low clustered in the eastern coastal areas. There were high-high clusters in the southern region, and low-low clusters in the northern region. In general, there was little significant difference between the geographical indications of poultry in China in the central and western regions.

Abstract:As one of the visual attributes of fish appearance, body length is a key factor related to the monitoring of fish growth status, regulation of water environment, feeding of bait drugs, quality and safety of fish products and the estimation of economic benefits. However, traditional body length estimation methods involve processes such as capture, anesthesia and manual measurement, which are time-consuming, labor-intensive and low-precision. In addition, it can also cause physiological stress responses and negatively affect the tested fish. With the rapid development of imaging technology, computing power and hardware equipment, non-destructive measurement methods based on machine vision have emerged rapidly, overcoming the limitations of traditional methods in terms of cost and performance. With its advantages of fast, accurate, timely, efficient and repeatable batch detection, it has become a powerful tool for fish body length measurement and plays a positive role in improving the economic benefits of aquaculture. The existing domestic and foreign research literature was summarized and sorted out, and the machine vision-based image acquisition equipment, fish contour extraction algorithms and length measurement methods were systematically analyzed and discussed. High-efficiency image acquisition and high-quality image data were important guarantees for accurate measurement. The advantages, disadvantages and applications of monocular cameras, binocular cameras based on optical imaging were firstly compared and analyzed. Secondly, the extraction of fish body contours from two parts of traditional image processing technology and image segmentation technology based on deep learning was summarized. Then, it was concluded that the underwater fish segmentation method based on deep learning had better robustness and versatility in the complex underwater scene. Using the image acquisition mode as the classification basis, the body length measurement methods based on the 2D mode and the 3D mode were described respectively. From the perspective of manual participation, the measurement methods based on the 3D mode were divided into automation and semi-automation. The semi-automation of stereo intersection methods such as DLT, template matching, and the Haar classifier were summarized. Also, convex hull algorithm, point cloud, and landmark point geometric morphology measurement method based on fully automated three-dimensional measurement methods were listed. However, due to the difficulty of deploying underwater cameras, the complication of underwater scenes, and the sensitiveness of the measured fish body, it was very challenging to apply machine vision technology to the measurement of fish body length widely. At last, the trend of fish body length measurement based on machine vision was proposed. Furthermore, image enhancement was the research focus, and fish contour extraction based on deep learning methods was the key technology. Also, developing length measurements based on 3D mode was the mainstream method and using three-dimensional point cloud data measurement and geometric features to fit contours was a direction. Machine vision combined with technologies such as deep learning, pattern recognition, and environmental perception, became a key method for obtaining fish growth information, which can provide technical support for the refined and intelligent management of aquaculture.

Abstract:With Chinese poultry production scale and quantity expanding, intelligent breeding is the current hot research direction of the regulation of poultry. Computer vision technology can be used to provide a noninvasive, non-invasive, low-cost and highly effective way to identify animals behaviors to detect the activity level, diagnose the diseases of chickens and find the dead ones. The visual systems of chicken detection and behavior recognition were summarized, and the correlativity between the phenotypic parameters, behavior parameters and reproductivity respectively, phenotypic feature extraction and recognition and behavior recognition algorithm were reviewed; the problems in visual system were analyzed, the optimization strategy was put forward; the feasibility of using computer vision technique to select high quality breeder rooster was discussed, and the rooster selection algorithm framework was preliminarily proposed. Finally, the applied prospects and the optimization directions of computer vision technology in the poultry industry were expected.

Abstract:In order to realize the rapid and accurate determination of the ammonia nitrogen content in the aquaculture water, the ammonia nitrogen in the water was taken as the research object, and the colorimetric sensing experiment was carried out on the basis of the salicylic acid spectrophotometry combined with the microfluidic technology to realize the quantification of the ammonia nitrogen content in the solution detection. By comparing the influence of four characteristic wavelength screening methods on modeling, it was found that the continuous projection algorithm selected the least wavelength variables, and the established prediction model had the best effect. The modeling set calibration standard deviation (RMSEC) and the prediction set calibration standard deviation (RMSEP) were 0.00931mg/L and 0.02857mg/L, respectively, and the relative analytical error (RPD) was 11.2141. The effects of pH value, salinity, temperature and reagent stability were explored, and it was found that the absorbance was increased first and then decreased with the increase of pH value. The salinity had a significant effect, and temperature can speed up the reaction, and the reagents can be stored for 14 days at 6℃ in dark. Under optimized conditions, the linear range and detection limit of the method were 0.04~0.92mg/L and 0.016mg/L, respectively. The standard recovery experiment was carried out on aquaculture water and seawater. The average recovery rate was between 95.8% and 116%, and the relative standard deviation was between 2.1% and 6.3%. The results proved the feasibility of the method of using salicylic acid spectrophotometry combined with microfluidic technology to detect ammonia nitrogen.

Abstract:Accurately obtaining the number of fish is a fundamental process for biomass estimation in fish culture. It not only helps farmers calculate the reproduction rate and estimate the production potential accurately but also serves as a guide for survival rate assessment, breeding density control, and transportation sales management. It can be said that fish counting runs through multiple links such as breeding, transportation, and sales. Among these links, fish live in different environments and their body size is also various, bringing certain difficulties to fish counting. Aiming at the above problems, a fish counting method based on multi-scale fusion and no anchor YOLO v3 (MSF-NA-YOLO v3) was proposed. Firstly, multi-source fish images were collected to construct a fish counting dataset with a total of 1858 images. Secondly, the feature extraction network of YOLO v3 was improved, and a feature extraction method based on multi-scale fusion was proposed to enhance the feature expression of fish images. Finally, the CenterNet was used as the detection network of YOLO v3, and then a fish target detection network based on no anchor was proposed to identify fish targets in images and realize fish counting. The collected fish counting dataset was randomly divided into a training set, validation set and test set. The training set and validation set accounted for 90% of the dataset, with a total of 1672 images, and the test set accounted for 10% of the dataset, with a total of 186 images. The ratio of the training set to the validation set was 9∶1, containing 1505 and 167 images, respectively. The MSF-NA-YOLO v3 fish counting model was trained and validated by using the transfer learning method. When the training loss and validation loss became stable, the training stopped and the best fish counting model was obtained. Based on this model, the fish images of the test set were counted and a precision of 96.26%, recall of 90.65%, F1 value of 93.37%, and average precision of 90.20% were achieved. Compared with the fish counting model based on the original YOLO v3 feature extraction method and the single scale fusion feature extraction method, the precision of the fish counting model based on the feature extraction method proposed was increased by 0.51% and 0.72%, respectively, recall was increased by 0.44% and 1.72%, respectively, F1 value was increased by 0.47% and 1.24%, respectively, and mean average precision was increased by 0.45% and 1.87%, respectively, indicating that the proposed feature extraction method had better performance. Compared with the fish counting method based on YOLO v3, YOLO v4, and ResNet+CenterNet, the recall was increased by 5.80%, 1.84%, and 3.48%, respectively, F1 value was increased by 2.26%, 0.33%, and 1.68%, respectively, and mean average precision was increased by 5.96%, 1.97%, and 3.67%, respectively. Thus, the proposed method had a good overall performance and can provide support for the realization of fishery automation and intelligence.

Abstract:Target detection is the key link of fish tracking, behavior recognition and abnormal behavior detection of fish body. Therefore, fish detection has important practical significance. Due to the low imaging quality of underwater surveillance video, the complicated underwater environment, and the high visual diversity of fish bodies, multi-target fish detection in complex background is still a very challenging problem. In order to solve the problem that the existing multi-target fish detection is mostly carried out in a controlled environment and the generalization ability is limited, a simple and effective multi-target fish detection model in complex background was proposed. The feature extraction method based on DRN was constructed by transfer learning. The features were extracted from the original image, and the candidate detection frame was further generated by combining RPN. A multi-target fish detection model in complex background was constructed based on Faster R-CNN. The experimental results on the ImageNet2012 data set showed that the detection accuracy of this model for goldfish in complex background reached 89.5%, which was much higher than the detection accuracy of the R-CNN+AlexNet model, Faster R-CNN+VGG16 model and Faster R-CNN+ ResNet101 model in this data set, indicating that this model can effectively and accurately realize the detection of multi-target fish in complex background.

Abstract:To improve the prediction accuracy of dissolved oxygen content in ponds, a novel long short-term memory (LSTM) optimized by an improved beetle antennae search algorithm (IBAS) was proposed. Firstly, Pearson correlation coefficient was used to obtain the linear correlation between each factor and dissolved oxygen. The key impact factors of dissolved oxygen were selected by Pearson correlation coefficient as the input feature, which can reduce the input dimension, eliminate the correlations of original variable, and improve the calculation efficiency of the model. Secondly, to balance the global search and local search, and improve the convergence speed of beetle antennae search algorithm (BAS), an IBAS with exponential decreasing strategy of attenuation factor was proposed, which linked the attenuation factor eta with the number of iterations. Finally, LSTM network was optimized by IBAS to get the best parameter combination strategy to construct a P-IBAS-LSTM prediction model between dissolved oxygen and these factors. Based on the presented model, the dissolved oxygen was predicted for an experimental pond during April 28 th to September 8 th, 2020 in the Research Center of Yixing City, Jiangsu Province. In the case of the same data, the mean squared error (MSE), root mean square error (RMSE), and the average absolute error (MAE) of the P-IBAS-LSTM were 0.6442mg2/L2, 0.8026mg/L, 0.5306mg/L, respectively. The experimental results showed that the proposed model of P-IBAS-LSTM had higher performance and stronger generalization performance when compared with common prediction models, which could meet the actual needs of predicting dissolved oxygen accurately and help farmers make decisions in ponds.

Abstract:At present, domestic locust monitoring is mainly based on manual monitoring, with low monitoring efficiency and inaccurate counting. In response to the above problems, the K-SSD-F algorithm, a video counting method of locusts, was proposed with the 5th instar migratory locust as the experimental object. This method can monitor the number of locusts in real time, continuously and automatically. Firstly, the KNN algorithm in the background separation method was used to extract the spatiotemporal features of the frames before and after the video; then the SSD model was trained through the labeled data, the video was detected, and the static features of the video were extracted, and the two were combined to improve the counting accuracy; finally, the frame compensation algorithm was used to recognize missing frames due to posture changes. The experimental results showed that the precision of locust identification was 97%, the recall rate was 89%, the average detection accuracy (mAP) was 88.94%, the F1 value was 92.82%, and the detection speed reached 19.78f/s. The proposed method had good robustness, which can realize real-time and automatic counting of locusts, its accuracy was better than that of other models, and it can also provide a theoretical basis for automatic identification and counting of other kinds of insects.

Abstract:At present, body condition score for dairy cows mainly adopts manual methods, but the reliability of the scoring results is poor due to manual subjectivity, and the assessment process is time-consuming and laborious, which relies heavily on the experience of experts. The development of body condition score for dairy cows has mainly gone through manual scoring stage, traditional machine learning stage and deep learning stage, the latter two can be subdivided into 2D field and 3D field research. Body condition score method for dairy cows based on machine learning mainly suffers from the problem of relying on manual markers and simply improving the method of dimensionality reduction and feature extraction, which can only be improved in specific situations, with limited improvement in results. With the rise of deep learning, researchers have begun to explore methods that do not require manually labeled features. The use of deep learning and 3D technology has further improved the accuracy of automatic body condition scoring, but in actual production, to meet the nutritional management needs of cows at different growth stages, the difference between the body condition score and the ideal score should always be maintained within ±0.25, and the accuracy of existing automatic scoring systems still has a certain gap with the ideal standard of actual farm management. The current research hotspots and theories of body condition score methods were summarized for dairy cows using computer vision by analyzing the literature and potential research directions were proposed. With the development of artificial intelligence, a large number of deep learning algorithms emerged that can be used for target detection and classification. These methods were also applicable to target detection and classification in the field of animal husbandry. In fact, artificial intelligence and deep learning techniques were increasingly being used in the livestock sector as well. Deep learning methods were needed for dairy cattle condition scoring, and as the development of agricultural information technology became more mature, research on automated body condition score methods for dairy cows based on deep learning would also become more advanced.

Abstract:Mastitis is a disease that affects the health of dairy cows. Timely detection of mastitis can improve the efficiency of mastitis treatment and reduce the economic loss of dairy industry. Aiming at the problem of low accuracy of thermal infrared technology in detection of cow mastitis, an improved YOLO v3-tiny algorithm was proposed to construct a model for automatic detection of key parts of dairy cows, and a model for automatic detection of key parts of dairy cows was constructed. The improved YOLO v3-tiny algorithm was based on the traditional YOLO v3-tiny. Firstly, the residual network was added between the convolutional layer and the pooling layer to increase the depth of network, so as to carry out deep level feature extraction, high-precision detection and classification. Secondly, the attention module of squeeze and exception (SE) was added to the key position of the network to strengthen the effective features and enhance the performance ability of the feature map. Finally, the performance of the activation function ReLU, Leaky ReLU and Swish was compared. It was found that the activation function Swish was better than the activation function ReLU and Leaky ReLU, so the activation functions in the convolutional layer of the backbone of the network model were changed to the Swish activation functions. The detection results of the improved model for key parts of dairy cows had the accuracy value of 94.8%, the recall rate value of 97.5%, the average detection accuracy value of 97.9%, and the F1 value of 96.1%. Compared with the results of traditional model, the accuracy value of the improved detection model was increased by 9.9 percentage points, the recall rate was increased by 1.7 percentage points, the average accurate detection accuracy value was increased by 2.2 percentage points, and the F1 value was increased by 6.2 percentage points, performance indicators were better than the traditional YOLO v3-tiny model, and it had little effect on the detection speed, which met the requirements of real-time detection. It showed that the algorithm can detect the key parts of dairy cows. And the target detection algorithm was used to conduct a dairy cow mastitis detection test. The obtained temperature difference was compared with a temperature threshold to determine the incidence of dairy cow mastitis, and the somatic cell count method was used to verify it. The results showed that the accuracy rate of dairy cow mastitis detection could reach 77.3%. It was proved that the method can achieve precise positioning of key parts of dairy cows and can be applied to detect dairy cow mastitis.

Abstract:In the process of real-time detection of pig body temperature based on thermal infrared video, a method for accurately detecting the ear root temperature of pigs was proposed to solve the problem that the head posture of pigs cannot accurately detect the ear root temperature. Firstly, according to the pig head movement trajectory data, the feeding pen channel was divided into the best ear root temperature measurement area; then, a position offset algorithm was proposed to detect the head posture correction frame in the best ear root temperature measurement area (head posture correct frame, HPCF). Finally, a pig head and ear root detection model based on YOLO v4 was constructed, and the pig head and ear root area were accurately positioned to realize the automatic detection of HPCF, and extract the left and right ear root detection of HPCF respectively. The highest temperature in the frame was taken as the root temperature of each ear. The test results showed that the average detection accuracy (mAP) based on the YOLO v4 model reached 93.15%, and the head and ear roots were positioned accurately; the HPCF detection accuracy rate were 91.33%; the ear root temperature extracted by the algorithm and the manually extracted ear root temperature were compared, and the results were analyzed. It showed that for the temperature of the left and right ear roots in the tested HPCF images, the errors of 97% and 98% of the test images were within 0.3℃. The above research results can provide technical means for real-time monitoring and early warning of abnormal body temperature of pigs.

Abstract:To improve the on-line monitoring rate of pig ear skin surface temperature and realize the long-term monitoring of pig ear skin surface temperature, a fast detection method based on optimal step for pig head was proposed. Firstly, five dynamic detection lines were designed to scan at the entrance of the channel. Secondly, as soon as the pig entered the channel, the optimal vertical step size was calculated by using the high temperature threshold and two vertical dynamic detection lines, so as to determine the position of the frame on the left and right sides of the pig head box. Finally, the results of the comparison between the high temperature threshold value and the temperature of the dynamic scan line in the vertical interval were used to calculate the optimal vertical movement step, and then the positions of the upper and lower edge lines of the pig head detection box were determined respectively, so as to realize the fast detection of the head based on the optimal step. The video data of 40 pigs were collected and tested on Matlab and C# platforms. The results showed that the average frame rate of the proposed method was 74.4% and 54.1% higher than that of the skeleton scanning strategy and the compressed sensing method, respectively. Compared with compressed sensing and kernel correlation filtering, the detection accuracy was improved by 11.03 percentage points and 13.82 percentage points, respectively. The mean error of ear base skin surface temperature was 0.235℃. The research result can provide technical support for the integration of the automatic detection system of pig body surface temperature.

Abstract:There are a variety of ions in soil suspension, so when using ion selective electrode to detect soil nitrate-nitrogen, it is particularly vulnerable to the interference of other ions, which will greatly affect the accuracy of detection. In order to improve the accuracy of monitoring soil nitrate concentration by ion selective electrode, it is necessary to establish a model to predict the concentration. The prediction performances of Nernst, SAM and BP-ANN models for soil nitrate concentration detection were discussed. The standard solution test was carried out to predict the prediction performance of the three models. Combined with the field corn monitoring experiment and potted pink crown tomato monitoring experiment to verify the sample experimental results, the soil nitrate concentration was taken as the output of the three models, and the effect of optical method on the determination of soil nitrate concentration was compared. The experimental results showed that the prediction results of the three models were in good agreement with the true values of the samples. Among them, the SAM model was the most accurate, and its correlation coefficients were not less than 0.9. The variation ranges of MAE, MRE and RMSE were 2.03~5.08mg/L, 0.64%~8.79% and 2.21~5.49mg/L, respectively. SAM concentration prediction model had the characteristics of high precision and good anti-interference. It can run on a relatively simple platform and had a wider range of application. In addition, when combined with the fluid control system, it can ensure the detection accuracy and improve the detection efficiency. It had a certain reference value for in-situ detection of soil nitrate nitrogen based on ISE.

Abstract:In order to realize the integrated monitoring of winter wheat crop-soil total nitrogen content, a winter wheat crop-soil common canopy hyperspectral feature wavelength selection method was proposed based on improved grey wolf optimization algorithm (IGWO). Totally 40 winter wheat fields at nodulation stage in Luohe City, Henan Province were used as the study area, and the improved grey wolf algorithm was used to select the winter wheat common crop-soil feature wavelengths by collecting wheat canopy reflectance spectra and combining with precise total nitrogen values measured in the laboratory. The results showed that the improved grey wolf optimization algorithm can select the common winter wheat crop-soil canopy reflectance spectra feature wavelengths compared with other bionomics optimization algorithms such as genetic algorithm (GA). Under the random forest (RF) regression model, the coefficients of determination (R 2) of the crop and soil test sets were 0.7888 and 0.7534, respectively. Compared with other bionomics algorithms, the IGWO selected the feature wavelengths of 405nm, 495nm, 582nm, 731nm and 808nm had the best prediction performance, these feature wavelengths can effectively use the full spectrum information and meet the physiological characteristics of winter wheat. The improved grey wolf optimization algorithm proposed can select the feature wavelengths of winter wheat crop-soil common canopy reflectance spectra to achieve a higher accuracy estimation of winter wheat crop-soil total nitrogen which can be an effective way to estimate winter wheat crop-soil total nitrogen content in the field.

Abstract:In order to improve the accuracy of integrated soil-crop total nitrogen detection in agricultural fields, the canopy spectra of winter wheat were used as a research object to quantify the accuracy of four data reduction methods (neighborhood preserving embedding (NPE), t-distribution stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE) and locally linear embedding (LLE)) in canopy spectral feature extraction and crop and soil total nitrogen content detection. The canopy spectral reflectance and the corresponding crop and soil total N contents of four varieties of winter wheat, namely Yumai 49-198, Zhoumai 27, Aikang 58 and Xinong 509, were collected at four levels of N application, respectively. The NPE, t-SNE, LE and LLE were used to downscale the data in the visible and partial near-infrared bands from 400nm to 900nm, and subsequently, a random forest regression model was developed based on the four sets of downscaled features. Comparison of the full-spectrum information and the prediction performance of the four sets of downscaled features for crop and soil total nitrogen content showed that the hybrid LLE-RF method achieved the best nitrogen prediction results with an R 2v value of 0.9150 for the coefficient of determination of crop total nitrogen content prediction and a root mean square error (RMSEP) of 0.2212mg/kg for crop total nitrogen prediction. The coefficient of determination R 2v for prediction of total soil nitrogen content was 0.8009. The RMSEP was only 0.0085mg/kg, which were all better than that of the original full-spectrum data as well as the other three sets of downscaled features. The experimental results showed that the LLE downscaled spectral information can effectively characterize the crop total nitrogen content and soil total nitrogen content.

Abstract:In order to solve the problem that the internal relationship between external features of color images and soil nutrients is ignored when hyperspectral technology is applied to quantitative detection of soil nutrients, a prediction model of soil total nitrogen content based on image and spectral features was constructed by combining the spectral information and image features of soil, and the prediction ability of image and spectral features fusion for soil total nitrogen content was explored. The hyperspectral images of soil samples were obtained by the laboratory hyperspectral imager, and the spectral information and image characteristics of soil were extracted from the hyperspectral images. The characteristic wavelength of spectral information was selected by using a joint algorithm of uniformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS), and the selected characteristic wavelength was used as soil spectral information. Through correlation analysis, image features with high correlation with soil total nitrogen content were selected. Categorical Boosting (CatBoost) algorithm was applied to the prediction of soil total nitrogen content, and the prediction of soil total nitrogen content based on single spectral information, single image feature and map feature fusion was compared. The results showed that the characteristic wavelengths selected by UVE-CARS joint algorithm were 942nm, 1045nm, 1199nm, 1305nm, 1449nm, 1536nm and 1600nm, which were consistent with the frequency doubling absorption of nitrogen-containing groups. The image features with high correlation with soil total nitrogen content were angle second moment, energy, inertia moment, gray mean and entropy. The model based on the characteristic wavelength of single spectral information established by CatBoost algorithm finally predicted that the total nitrogen content of soil R 2 was 0.8329 and RMSE was 0.2033g/kg, the model based on image features finally predicted that the total nitrogen content of soil R 2 was 0.8017 and RMSE was 0.2197g/kg. And the model based on fusion of image and spectral features finally predicted that the total nitrogen content of the soil R 2 was 0.8668, and RMSE was 0.1602g/kg, the prediction accuracy was higher than that of single spectral feature and single image feature. Compared with the prediction model based on single spectral feature and single image feature, the prediction model based on hyperspectral atlas feature fusion had better effect, which can provide a method for the prediction of soil total nitrogen content.

Abstract:In order to obtain the information of soil organic matter content quickly and non-destructively, a portable instrument for measuring soil organic matter content was designed based on spectroscopy principle. The detector was mainly composed of mechanical part, optical path system and control part, in which the mechanical part provided platform support for the detector, while the optical path part consisted of light source, sapphire glass, filter and photoelectric detector, and the control system realized the collection and processing of soil measurement signals. When the portable soil organic matter detector worked, the light emitted by the light source irradiated the surface of the soil to be detected, the diffuse reflection light was filtered by the optical filter, and then converted into an electrical signal by the photoelectric converter, and then the reflectivity at each sensitive wavelength was calculated by the signal processing unit, and the content of soil organic matter was detected by measuring the spectral reflectivity. The spectral data of soil and the measured values of soil organic matter content in Beijing Shangzhuang Experimental Station were collected. After preprocessing the spectral data, the processing results of four wavelength screening algorithms, CARS, MCUVE, MWPLS and Random Frog Leaping, were compared, and the partial least squares and random forest prediction models of soil organic matter content were established. The results showed that the random forest model based on four characteristic wavelengths selected by CARS algorithm had the best prediction accuracy, with the modeling set R2 being 0.923 and the prediction set R2 being 0.888. The CARS-RF model was embedded into the organic matter detector system. The experimental results showed that the correlation coefficient between the measured value and the standard value of the detector reached 0.891. The developed detector had high precision and can quickly detect the content of soil organic matter.

Abstract:The soil bulk density of the topsoil layer is an important parameter of farmland soil, and it is of great significance to accurately measure and evaluate it. A vehicle-mounted surface soil bulk density detection system based on Raspberry Pi was designed. The system took soil surface images and predicted the surface soil bulk density using easily-obtained soil surface image features. Extracted the Tamura texture feature of the image and the fractal dimension feature of the image. After verification, the roughness, contrast, directionality, and fractal dimension features were highly correlated with soil bulk density, and the correlation coefficients were -0.754, -0.799, -0.806, and -0.849. So these four parameters were selected as the input of the prediction model. SVM regression model, GRNN regression model and Bagging integration model based on SVM and GRNN were used to predict soil bulk density. Based on the correlation analysis between the prediction results of the Bagging integration model of SVM and GRNN and the results obtained by the ring knife method, R 2 reached 0.8641, and the average absolute error (MAE) of the prediction results reached 0.0316g/cm 3, and it had better prediction results than a single SVM regression model and a single GRNN regression model. The field test was carried out using the soil bulk density detection system of farmland topsoil based on Raspberry Pi. And the results showed that the average absolute error (MAE) of the measurement was 0.0412g/cm 3, which was in line with expectations and met the requirements of accurate and rapid detection.

Abstract:For the vehicle-mounted soil conductivity measurement system based on the current-voltage four-terminal method, the excitation source will fluctuate with the change of soil load, thereby affecting the measurement results. Using waveform standard and strong anti-interference ability adjustable frequency and amplitude modulation digital signal generator instead of ordinary excitation source, by exploring the influence of different frequencies and amplitudes of the signal generator on the experimental results, and then the most suitable frequency and amplitude were fond for the vehicle-mounted measurement system value. In order to reduce the impact of complex environment of field on the experimental results, the experiment was divided into laboratory exploratory experiments and field confirmatory experiments. Laboratory exploratory experiments set five amplitudes and 20 frequencies for experiments. By analyzing 6000 sets of laboratory data of 60 sets of samples, it can be known that the larger the amplitude at the same frequency was, the better the experimental effect was, and the best amplitude was 10V. Under the same amplitude, with the increase of frequency, the soil conductivity showed a parabolic trend which was increased first and then decreased. The measured value of the soil conductivity and the reference value determination coefficient R 2 reached 0.9505 at the amplitude of 10V and frequency of 100Hz, and the experimental effect was the best. The best amplitude of the field confirmatory experiment was still 10V, but because the soil load was increased with the increase of the electrode spacing, the best frequency of the field confirmatory experiment became 1kHz, with an amplitude of 10V and a frequency of 1kHz. The measured value of conductivity and the reference value R 2 were 0.8484, which was the best experimental effect.

Abstract:With the continuous development of soilless cultivation technology in China, the testing of the internal components of hydroponic nutrient solutions, which are a key part of soilless cultivation technology, has become a hot topic of research. Magnesium ions (Mg 2+) are essential nutrients for important physiological processes such as photosynthesis, respiration, and the synthesis of genetic material in plants, so the accurate detection of Mg 2+ content in soilless nutrient solutions is of key significance for the regulation of crop production. A summary of the available literature showed that Mg 2+ detection had evolved from traditional atomic absorption to current fluorescent probe detection techniques, but Mg 2+ fluorescent probe materials were complex to synthesize and were susceptible to interference from calcium (Ca 2+) and zinc (Zn 2+). Salicylaldehyde, a readily available and inexpensive intermediate for commercial fine chemical synthesis, can be combined with Mg 2+ to form a stable chelate for Mg 2+ detection. A salicylaldehyde fluorescent probe combined with a microfiber optic spectrometer was used for the detection of Mg 2+ in nutrient solutions to investigate the detection mechanism. The sensitivity, selectivity, and interference resistance of the molecular probe were systematically determined, and the reproducibility of the salicylaldehyde fluorescent probe was investigated through the measurement and spiked recovery of strawberry nutrient solutions. Experimental results showed that the fluorescent probe binded to the magnesium ion to form a stable complex, producing chelated fluorescence enhancement. The fluorescence response intensity of the salicylaldehyde probe was well linear in the concentration range of 0~800μmol/L Mg 2+, with a correlation coefficient of 0.999 and a response time of about 2min, enabling the rapid determination of Mg 2+ in solution. The probe showed good selectivity for Mg 2+, with the fluorescence response intensity of equal concentrations of Ca 2+ and Zn 2+ ions increasing by only 2.5% and 9.1% of the Mg 2+ intensity year-on-year, with a weak fluorescence enhancement compared to the blank group. At the same time, the probe had good resistance to interference, and the fluorescence intensity measured by solutions of Mg 2+ coexisting with Ca 2+ and Zn 2+ coexisting with Mg 2+ in equal amounts had a year-on-year growth rate between -0.004 and 0.009 compared with the fluorescence intensity measured by a single Mg 2+ solution salicylaldehyde fluorescent probe, with the growth rate fluctuating up and down by less than 0.01. The five solutions of Ba 2+, Cu 2+, Mn 2+, Fe 2+, and Fe 3+ showed fluorescence intensity growth rates ranging from -0.21 to -0.91 year-on-year, with a fluorescence burst compared with the pure water blank group. The spiked recoveries of the actual samples ranged from 99% to 104.9% with a relative error of less than 1.33% and an RMSE of 5.78μmol/L, demonstrating the good sensitivity and reproducibility of the salicylaldehyde fluorescent molecular probe for the determination of Mg 2+ concentrations in nutrient solutions. In conclusion, the research result initially validated the feasibility of salicylaldehyde fluorescent probes for Mg 2+ detection in nutrient solutions.

Abstract:Wireless remote control technology plays a very important role in agricultural production, but China still uses the traditional production mode in agricultural production, which will cause a lot of human and material resources waste, at the same time, it is not conducive to the sustainable development of agriculture. Remote control technology can automatically, efficiently and accurately control the terminal equipment, so as to liberate manpower from the traditional mode of production. Therefore, the application of modern industrial control technology, wireless communication technology and Internet of Things technology to agricultural production can not only improve agricultural production management mode, but also can improve agricultural production efficiency, and help to transform modern agriculture into smart agriculture. The application of remote control technology in agriculture at home and abroad was reviewed, and the problems existing in the current use of remote control technology in agriculture were analyzed and summarized. At the same time, the problems and solutions in ZigBee technology, WiFi technology, LoRa technology, NB-IoT technology and 5G technology were summarized. Finally, it was pointed out that the application of 5G technology to agricultural remote control was the future research direction, that was, the use of sensors and other technologies to collect data and information. 5G technology shortened the time for data acquisition and transmission and expanded the space for data collection.

Abstract:To achieve high efficiency and accuracy of simulation, a multi-mode voltage-behind-reactance (MVBR) induction machine model was proposed based on frequency shifting theory. At first, the analytic signals were constructed by using Hilbert transform. Frequency shifting was performed, and multi-scale VBR and approximate VBR (AVBR) models were developed. Meanwhile, shift frequency was introduced as a novel parameter in the proposed MVBR model. By appropriate selection of shift frequencies, accurate and efficient multi-scale transients simulation was supported. When the shift frequency was set to be zero, MVBR model performed in the same way as AVBR model. In this mode, high-frequency transients were simulated accurately with small time-step sizes. The conductance matrix of machine model remained constant, thereby contributing to improvement in simulation efficiency. When the shift frequency equaled the carrier frequency, multi-scale VBR model was selected. Large time-step sizes can be used in the simulation of low-frequency transients and steady-state. The test case demonstrated the effectiveness of the proposed machine model in terms of accuracy and efficiency.

Abstract:Photovoltaic poverty alleviation not only makes full use of solar energy resources, but also promotes rural poverty alleviation and development and new energy utilization. There is a lack of research on the optimal allocation of rural comprehensive energy. In view of the problem that Chinas rural comprehensive energy construction cannot copy the urban energy structure. Based on the background of Chinas rural photovoltaic poverty alleviation, combined with the rural biogas recycling mode, a rural integrated energy station with the characteristics of rural industrial development was innovatively designed, and the optimization allocation method was put forward. Considering the cost and benefit of photovoltaic poverty alleviation, biogas recycling and the constraints of electricity, heat and cold in rural energy stations, the model optimized the capacity of gas turbine, gas boiler, electric cooler and energy storage device. Taking a poverty-stricken village in the north as the research object, using the local photovoltaic poverty alleviation policy support and the characteristics of rich biomass resources, the rural integrated energy station equipment was optimized, and the income of rural integrated energy station based on photovoltaic poverty alleviation was analyzed. The results showed that the optimal allocation of rural integrated energy station proposed was reasonable and effective, which can provide theoretical and technical support for future rural integrated energy planning and green industry poverty alleviation.

Abstract:With the scale access of distributed generations and electric vehicles, the operating state of smart distribution networks is becoming more and more complex and changeable, presenting normalized uncertainty and volatility, and puts forward higher requirements for coordinated optimal control methods. Aiming at the problems of distributed generation output randomness restricting its consumption and utilization, and the disordered charging of electric vehicles exacerbates peak loads, a coordinated optimal control method for smart distribution network considering orderly charging of electric vehicles was proposed, based on distributed generation output and load forecasting results. The method mainly dynamically optimized the charging time, charging sequence and charging position of electric vehicles that responded to the time-of-use electricity price to efficiently match the randomness and volatility of distributed generation output. A multi-objective optimal control model was established, which considered the consumption of distributed generation, peak and valley difference of load and peak load, charging cost and satisfaction degree of electric vehicle users. The hybrid optimization algorithm of particle swarm and non-dominated sorting genetic algorithm Ⅱ was used to solve the model. Finally, the feasibility and effectiveness of the proposed model and method were verified by simulation results.

Abstract:User profile as a tool for accurately analyzing users’ characteristics and behavior, has received more and more attention from both academic and industry in recent years. The basic concepts and features of user profile was presented, a comprehensive introduction to the key technologies of related work was conducted, and the advantages and disadvantages of different technologies were compared. Furthermore, the applications of user profile technology in agricultural field were reviewed, including describing farmers’ characteristics, personalized recommendation of agricultural services, precision marketing of agricultural products, and decision support for agricultural management. Finally, the existing problems of user profile technology were summarized, the trend of future research and the application prospect of user portrait technology in agriculture were discussed.

Abstract:In order to improve the prediction accuracy of the rabbit house environment parameters, solve the coupling relationship between environmental parameters ignored in traditional predict method, and reduce the cost of rabbit house environmental control, a multivariable environmental prediction sequence to sequence model of rabbit house based on Long Short-Term Memory was proposed. Double-layer LSTM was used as the encoder and decoder of the Seq2Seq structure to improve the characterization ability and prediction accuracy of the environmental parameter prediction model. The Seq2Seq structure can not only effectively extract the time correlation of the rabbit house environmental parameter sequence itself, but also can mine the coupling relationship between the parameters. The model was used to test and predict the data of temperature, humidity and carbon dioxide concentration in the rabbit house which in a rabbit farm in Shengzhou City, Zhejiang Province. The results showed that the multi-parameter prediction model of the rabbit house environment achieved good prediction performance. Compared with standard LSTM model and standard SVM model, the prediction accuracy of temperature is improved by 28.41% and 48.60%, the prediction accuracy of humidity is improved by 9.84% and 56.08%, and the prediction accuracy of carbon dioxide concentration is improved by 5.39% and 11.19%. The experimental results showed that the proposed multivariable environmental prediction model of rabbit house not only had good forecasting effect, but also can meet the needs of accurate of prediction of rabbit house environmental data.

Abstract:There are two kinds of method for the science popularizing of intelligent greenhouses: teaching with slides and on-site visits. Teaching with slides is not intuitive and the cost of on-site visits is high. Also, the human-computer interface of the intelligent greenhouse control system is not attractive. Aiming at above problems, an intelligent greenhouse science popularizing system based on virtual reality was developed. The functions of the system included: three-dimensional scene display of greenhouse, three-dimensional simulation of greenhouse equipment, and interaction of greenhouse equipment. The system used a 4-layer software architecture system: model layer, Unity3D service layer, business logic layer and presentation layer. A comparative experiment of virtual reality teaching and slide teaching was conducted. The experiment compared the teaching effects of the two methods through a post-questionnaire survey, and also evaluated the usability of the science popularizing system.The results of the experiment showed that 100% of students who used virtual reality for learning believed that they remained focus during the learning process, 93.3% of students preferred virtual reality to presentations on slides, and 33.3% of students think they had a strong sense of knowledge acquisition.Furthermore, their average score on objective questions was 13.96% higher than that of students who used slides to study.The above results showed that the science popularizing system had high usability.

Abstract:Light is the main energy source of solar greenhouse. The change of outdoor light and temperature will cause internal environmental parameters. Light intensity, air temperature and humidity are important factors affecting the rate of plant photosynthesis. Therefore, it is important to study the changing trend of light intensity, air temperature and humidity in greenhouse to guide crop production in greenhouse. In this experiment, the sunken solar greenhouse was taken as the research object, and the wireless sensor network was used to realize the real-time collection of light intensity and air temperature and humidity. Through the analysis of the variation trend of the light intensity, air temperature and humidity, the variation trend of light intensity at the observation points can be obtained, which were located on the south (S), north wall (N), east (E), west (W), middle (M) of the greenhouse interior and outdoor (O) of the greenhouse, also the variation characteristics of air temperature and humidity at different locations within 1/2 section of the greenhouse span. The results showed that the average of light intensity at daytime during summer sunny weather in descending order was as follows: observation points O (68267lx), S (53359lx), M (44770lx), W (44141lx), N (38907lx) and E (28615lx). The light environment in the north and south direction was more similar to that in the east and west sides. The average of temperature at daytime in the greenhouse was higher than 35℃, and the average of relative humidity at daytime was lower than 50%, which was harmful to crops. On cloudy days in summer, the overall light in the greenhouse was weak, but the overall temperature, light and wet environment of the greenhouse was more uniform than that on sunny days in summer. In the area around the north wall, the light was obviously insufficient (the average of light intensity at daytime was 7985lx).

Abstract:In view of the existing problems in traditional greenhouse, such as difficulties in remote operations, necessity of manual intervention in data collections, and low intelligence in production, an intelligent edge Mesh sensor network was constructed based on edge computing, and a cluster head selection method for temperature and humidity sensor network in greenhouse was proposed based on improved LEACH algorithm. The temperature and humidity sensor nodes were constructed by using ESP8266-12F wireless module, NodeMCU type based Internet of Things extension board and DHT-11 temperature and humidity sensor, and the automated data acquisition algorithm was developed. Based on the ESP8266-12F wireless module, the edge wireless Mesh sensor network was constructed, and the automatic networking between nodes was achieved. Aiming at the problems of large load consumption and low signal transmission rate of central router, a method of dividing the network based on RSSI value of central router was proposed, which improved the transmission rate of the network. A temperature and humidity sensor network Sink cluster head selection algorithm was proposed based on LEACH algorithm, which was suitable for the plant factory temperature and humidity sensor network deployment and beneficial to balance the overall energy efficiency of the network. The simulation experiment results showed that when the probability of the initial cluster head was set to be 0.1, by using the original LEACH algorithm, the probability was 10.86% for the cluster head appearing in the center of the sensor network; however, it was increased to 17.42% when using the improved LEACH algorithm with weight k=1; and the probability would be further increased to 24.96% for the cluster head appearing in the center position when using the improved LEACH algorithm with weight k=2.

Abstract:In order to predict the degree of water stress of tomato in greenhouse, sensors were used to obtain the internal environmental information of greenhouse, including air temperature (Ta), air relative humidity (Rh), substrate humidity (Hs), light intensity (Li), carbon dioxide concentration (CO2) and substrate temperature (Ts). The wind speed (Ws), outdoor relative humidity (Rho) and outdoor air temperature (Tao) of the greenhouse were obtained from local weather station. According to the above nine parameters, the crop water stress index (CWSI) prediction model of greenhouse tomato was established based on CS-CatBoost. The feature weights were calculated and screened by the gradient lifting algorithm. The performance of the CS-CatBoost algorithm under different input feature numbers was compared with the original CatBoost model, CS-LightGBM model and CS-RF model. The results showed that when the number of input parameters of the model was 7, compared with CatBoost, CS-LightGBM and CS-RF, the RMSE was decreased by 0.0123, 0.0118 and 0.0311, MAE was decreased by 0.0066, 0.0075 and 0.0208, MAPE was decreased by 0.9630, 1.1232 and 3.0892, while R 2 was increased by 0.0177, 0.0165 and 0.0767. When the number of other parameters as the model input, CS-CatBoost models prediction ability was superior to the other three model. The research result proved that the CS-CatBoost model had better prediction ability and generalization ability, which provided a strategy for water stress degree analysis of greenhouse tomato cultivation, thereby improving the utilization efficiency of agricultural water resources.

Abstract:Aiming at the problems of rapid deterioration of the components, low screening efficiency and high energy consumption of the existing drum screening equipment in the composting industry, a trommel screening machine for composting was designed, a transmission mode was designed, and a screen cleaning device was added. The fraction model of the trommel screening machine was constructed, the structural parameters of the key components were determined. In order to explore the performance of trommel screening machine, some relevant tests were carried out. Firstly, the three factors experiment was employed with feeding capacity, rotation speed and angle of trommel as the impact factor. Then based on the above screening effect, the experiment of three factors and five levels was executed, so as to study the influence law of screening efficiency and average power on screening performance and achieve the optimal combination of each factors. The results indicated that the trommel screening machine had the optimal screening efficiency and the power consumption, which were 96% and 2.55kW, respectively, when the feeding rate was set at 39.6t/h, the trommel speed was set at 12.4r/min and the trommel angle was set at 5.6°. Through the verification test, the screening efficiency was 95% and the power consumption was 2.69kW under the influence of the same factors. The relative errors were 1.1% and 5.5% with the theoretical results, which could satisfy the requirements of material screening quality.

Abstract:In view of the cow dung in the process of aerobic fermentation production of biological organic fertilizer, due to improper parameters of the fermentation cycle is long, the poor quality of fertilizer, and fruit branch waste recycling use difficult problems. Cow dung and walnut twigs were used as raw materials, carbon-nitrogen ratio, fungicide and the number of days between turns were taken as the experimental factors, germination index, organic matter content and total nutrient content were used as test indexes. On the basis of a large number of single factor tests, selecting the optimal level of multi-factor orthogonal test, the optimal combination process was determined. Test results showed that when the carbon nitrogen ratio was 25, inoculants bacteria agent B, the number of days between turns were 4 days, aerobic fermentation of cow manure heated up quickly, high temperature maintenance time was long, fermentation cycle was short, the organic fertilizer produced by fermentation had a high degree of maturity fermentation biological. The effective viable count (cfu) was 2×108 per gram, the total nutrient content was 14.86%, and the germination index was 96.6%. The results can provide the necessary theoretical basis for the large-scale treatment of cow dung and the resource utilization of walnut twigs.

Abstract:Due to the irregular shape of shrimp, the automatic orientation is difficult. In order to solve the problem that the orientation link of shrimp pretreatment still relies on manual operation and does not realize automatic operation, taking Penaeus vannamei as the research object, a shunting orientation method of abdomen and back for deheading shrimp was proposed. This method was a directional way of detection before sorting. In principle, it was different from the orientation method relying on friction and gravity. According to the principle that there were differences in the slope of the abdominal and dorsal curve of shrimp, the shunting orientation method of abdomen and back detected the ventral and dorsal orientation. Then the shrimp with different ventral and dorsal orientations were selected to achieve the same ventral and dorsal orientations. Theoretical analysis showed that the two-line photoelectric sensing method can be used to judge the ventral and dorsal orientation of shrimp. By controlling the transportation gap of shrimp, shrimp can be sorted, and finally the orientation of the abdomen and back of shrimp can be realized. According to the orientation method of abdomen and back for shrimp, the orientation system of abdomen and back for shrimp was designed, and the hardware construction and software development of the system were completed. The hardware of the system was mainly composed of photoelectric sensor, input and output module, computer and shunt mechanism. The test results of the detection accuracy and the orientation success rate showed that the average detection accuracy of large shrimp was 96.9% and the average detection accuracy of medium shrimp was 98.8%. And the average orientation success rate of large shrimp was 95.4% and the average orientation success rate of medium shrimp was 97.5%.

Abstract:The commonly used shrimp peeling equipment is roll shaft shrimp peeling machine. However, the machine can not adapt to shrimp material feeding information in real time and lacks automatic control system. Aiming at the problems of poor material adaptability, low degree of automation, lack of parameter acquisition ability existing in the roller shrimp peeling machine, the material information detection method and equipment operation parameter detection method based on machine vision technology were studied, and the parameter detection and control system of the roller shrimp peeling machine was designed. The system was mainly composed of image acquisition module, parameter detection module and control module, which can realize the real-time detection and optimal control of the main parameters of peeling machine. By analyzing the relationship between the number of shrimp pixels and the quality of shrimp, combined with the range of shrimp size, the detection model of shrimp size and feeding rate was proposed. According to the working characteristics of the peeling machine, the detection methods of roll shaft rotation angle and speed, tappet frequency, raw material lifting belt speed and water flow rate were proposed. The results of parameter monitoring test showed that the monitoring errors of shrimp size, feeding rate, roll rotation angle, roll rotation speed, tappet frequency, raw material lifting belt rotation speed and water flow rate were 0, 3.46%, 0.51%, 1.73%, 1.93%, 3.34% and 0.92%, respectively. The control errors of roll rotation angle, roll ratation speed, tappet frequency and raw material lifting belt ratation speed were 0.53%, 1.04%, 2.15% and 3.34%, respectively.

Abstract:Chinese hickory shell is the endogenous foreign body in its kernel production. Since the shell and kernel is similar in color, it is difficult to distinguish the shell and kernel accurately by color. In order to solve this problem, a nondestructive method based on hyperspectral imaging and deep learning for detecting endogenous foreign body in Chinese hickory nut was proposed. According to composition and structure of hickory nut, all samples can be divided into the inner shell, outer shell, inner kernel and outer kernel groups. After the hyperspectral images of each group was collected, the background of each hyperspectral image was removed by the Otsu method morphological algorithm and logical ‘and’ operation. The spectra of pixels in each group were extracted and preprocessed by multiplicative scatter correction (MSC) method. The deep features of the spectra were extracted by one dimension convolutional neural networks (1DCNN) and an 1DCNN model was established for detection of endogenous foreign body in hickory nut. To improve the detection accuracy, the spectra of pixels were normalized and reshaped into two-dimensional vector as the input of two dimension convolutional neural networks (2DCNN). The performance of the proposed 2DCNN model was better than that of the 1DCNN model. The accuracies of 100% and 98.5% were achieved for the training set and testing set, respectively.

Abstract:Ensuring the quality and safety of iced aquatic products is the key to improve the benefits of the aquatic industry. Traditional aquatic product freshness evaluation faces the following challenges: complicated operations, samples destruction and low efficiency. An effective and scientific method is urgently needed for aquatic products cold chain system. To solve the above problems, an improved object detection network SSD was proposed to realize the sensitive area location and pomfret freshness evaluation. Firstly, image datasets of iced pomfret freshness grade was established based on environmental factors-pomfret image-total volatile basic nitrogen (TVB-N). The image data of pomfret was collected according to the physicochemical index TVB-N of pomfret freshness at a constant temperature of 0℃, with days as the unit of time. Then, the samples of pomfret images were expanded with data augmentation and marked by LabelImg, annotated image datasets of iced pomfret were provided for freshness detection. Secondly, based on prior knowledge, the eyes and gills of the pomfret were chosen as region of interest. Considering the trade-off between detection accuracy and speed in cold chain application, one-stage object detection network SSD performed better. SSD significantly improved the performance by replacing the backbone network and designing adaptive prior boxes. The improved SSD reached mean average precision of 98.97% and 99.42% on the golden and silver pomfret datasets respectively and the detection speed reached 37 frames per second. The results met the demand for real-time and assessment accuracy in application scenarios, and enabled low-cost, efficient and accurate assessment of pomfret freshness.

Abstract:In the real production and sales scenarios of agricultural products, the network structure formed by consumers co-purchase behavior is very complex and changeable. Although the community discovery algorithm can effectively dig out the hidden information behind the co-purchase behavior, there are problems that the analysis results are not easy to understand, and the supporting decision-making conditions are insufficient. Because of the widespread application of community discovery algorithms in the analysis of co-purchase networks, and the ability of visualization technology to present the analysis results, a visual analysis method for the co-purchase networks of agricultural products based on community discovery was proposed. Firstly, the method used the community discovery algorithm Clauset-Newman-Moore (CNM) to divide the agricultural product co-purchase network. Secondly, the quantity of agricultural products in different communities in the network structure, the frequency of co-purchase behaviors, and the proportion of the price mode of agricultural products were analyzed, and then interactive analysis on the information of customers who co-purchase a certain agricultural product in each community was conducted. Finally, the analysis results were displayed interactively and visually. According to the visual interface, some behavioral rules of co-purchases were obtained, and then their consumption rules were deeply explored. In order to better present the visual analysis method, a set of dynamic sales data of agricultural product in Qingdao area were interactively explored and analyzed through the design of a visual analysis interface, and the sales model found can not only inspire the improvement and optimization of the manufacturers marketing methods, but also can help consumers to better choose agricultural products that suit them.

Abstract:Foodborne diseases are of major concern for public health. A study on pork products was undertaken to assess the safety status of pork products in China market based on spot check data of pork products in China from 2015 to 2019 (n=22340). The results showed that the percentage of unqualified pork products was decreased from 10.87% in 2015 to 2.44% in 2019 which meant the safety of pork products in China was improved greatly in recent years. According to the statistics on the main factors affecting the safety of pork products, it was found that additives were the main factors affecting the safety of pork products since the detection rate of the additive was 1.62%. It was mainly affected by the type of pork products and the production process. The results also showed that microbial contamination and veterinary drug residues were the main factors affecting the safety of pork products. However, different from microbial contamination, which was affected by season and temperature, the level of veterinary drug residues was mainly related to animal diseases. In addition, heavy metal pollution may have a lasting impact on product safety in a long period of time compared with other factors for it was closely related to environmental pollution of raw material cultivation, although it accounted for only 0.17% of all sampling products.

Abstract:With the rapid development of information technology, packaging and logistics technology, the range and scale of E-commerce products, including agricultural products, are getting larger and larger. At the same time, the online shopping review data has grown exponentially.The online reviews has become a hotspot. Taking JDs E-commerce platform as an example, the online reviews were mined out and the sentimental tendency of consumers about eggs consumption was analyzed deeply. The main research contents included proposing a domain sentimental lexicon with machine learning (DSLML) classification method. The semantic orientation pointwise mutual information (SO-PMI) method was used to construct the domain sentimental lexicon, and then a machine learning model was selected as the classifier to achieve the classification of sentimental orientation of online egg reviews. Then the LDA topic model was constructed to mine out the positive and negative topics in egg reviews. The experimental results showed that the DSLML classification model was improved in each indicator of text sentimental tendency classification, compared with machine learning models and domain sentimental lexicon (DSL) alone. From the results of the theme mining, the quality of eggs and the packaging of goods were the two aspects that consumers mostly concerned about. The conclusion of this research can provide data support and theoretical support for egg E-commerce operators to improve business strategies and service quality.

Abstract:Deeply mining and analysis of E-commerce online comments of fresh agricultural products is of great significance for reducing consumers’ perceived risks and assisting consumers in making decisions. The research progress of online comments acquisition methods, text preprocessing methods, text representation methods, and text sentiment analysis was firstly reviewed based on different models and technologies. Then the latest research results such as the influence mechanism of the comments on the sales of fresh agricultural products, the information and emotional attributes of the comments, and the influence of the contradictory comments on online sales of agricultural products were analyzed. Finally, it was proposed that future research should further focus on improving the quality of data, fusing multi-modal data in comments, and studying the role of emoji in emotional expression.

Abstract:Raw aquatic products carry micro-organisms, parasites and germs that are harmful by-products to the quality of raw aquatic products, in order to extend the shelf life of raw aquatic products and ensure their safety for consumption, sterilization is essential. Different types of aquatic products require different sterilization intensities, too low will result in incomplete sterilization affecting their shelf life, too high will damage the protein quality of raw aquatic products affecting their taste, so the intensity needs to be intelligently regulated. An intelligent UV intensity control sterilization system for raw aquatic products was designed, including a sensing layer, a signal processing layer, a data collection and collation layer and an analysis and execution layer. The sensing layer collected, stored and uploaded real-time information on raw aquatic product categories and environmental information on the processing line to a cloud database, comparing the sterilization data to determine the sterilization level and automatically adjusted the UV sterilization intensity to achieve an automated production line of aquatic product classification and grading sterilization. The oyster processing line was used to sterilize the oyster surface with UV intensity and the changes in the volatile salt nitrogen content of the oyster, a traditional indicator of oyster quality, were measured under the same conditions. The results showed that this intelligent UV sterilization system was effective and had a significant effect on the shelf life of raw aquatic products.

Abstract:Recognizing named entities from raw text is the first step to construct a fresh egg supply chain knowledge graph and support a variety of downstream natural language processing tasks. This task can sort out the information in the supply chain and provide a basis for food safety traceability. In the raw text of fresh egg supply chain, there were various types of entities, and feature information extraction was inefficient. In order to solve the problem of fast and accurate identification of the named entities which entity types were pre-defined, a bidirectional encoder representations from transformers-conditional random field (BERT-CRF) architecture was proposed to solve the task of named entity recognition (NER) in the area of fresh egg supply chain. In BERT-CRF architecture, begin, internal and other (BIO) labeling rule was used to label the sequence, and the concatenation of character vector and position vector was used as inputs. The pre-training language model (BERT) was used to obtain the global features of input sequence, and the CRF layer was added at the end of the model to introduce hard constraints. A comparative experiment was conducted with other three NER model on the self-constructed dataset that contained five categories and 21 subcategories. The result showed that the BERT-CRF model was superior to the others and reported a state-of-the-art performance. The precision, recall and F1-score were 91.82%, 90.44% and 91.01%, respectively. Finally, through the comparative experiments with other self-constructed dataset (dish dataset), the results showed that the model had a certain generalization ability.

Abstract:Aiming at the problems of rapid test verification and equivalent road test accuracy of agricultural transport vehicle braking performance, a vehicle braking performance test bench was developed, which can realize the non-disassembly test of agricultural transport vehicle. Based on the energy distribution of vehicle braking process, the electromechanical inertia coupling compensation mechanism was proposed. Based on the coupling compensation of the mechanical inertia for the rotating parts and the electrical inertia of the motor output, the stepless simulation of the tested vehicle inertia was realized, and the energy transfer distribution of the agricultural transport vehicle in the braking process was matched. The vector double closed-loop control system of speed-torque dual-input motor was established to improve the control accuracy of motor output torque. Based on Matlab/Simulink, the simulation model of electromechanical inertia coupling compensation for agricultural transport vehicle-test bench was established. The output parameters of conventional braking from the simulation model were compared under pure mechanical inertia compensation and electromechanical inertia coupling compensation, which verified the effectiveness of electromechanical inertia coupling compensation mechanism. The hardware of the braking performance test bench and the distributed measurement and control system of the upper and lower machines were built, and the inertia compensation comparative tests were carried out based on a certain type of agricultural transport vehicle. The test results showed that the average deviation of the front and the rear axle velocity was 1.539km/h, and the variance of the velocity deviation was 1.730 km 2/h 2. Based on the coupling compensation of electromechanical inertia, which can match the energy transfer of the tested agricultural transport vehicle in the braking process in real time, and it can improve the effectiveness of the vehicle bench test and the accuracy of the equivalent road test.

Abstract:With advantages of ride comfort and fuel economy, the hydro-mechanical continuously variable transmission (HMCVT) technology has been widely used. A 176kW tractor HMCVT sketch with single row planetary gear structure and double-session was introduced as a mathematical model to study system characteristics. The theoretical model of transmission ratio distribution was established, and the possible power flow directions for the HMCVT were researched with the changing of pump displacement ratio. For display of steady characteristics and prediction of output efficiency, efficiency expression of the HMCVT was obtained by study of torque ratios of the epicyclic gear train (EGT) links, and by study of torque ratio of the hydrostatic transmission system (HST). Through solving the equations of flow continuity of the HST and torque balance of transmission input shaft, the HST and the EGT systems were connected organically, a numerical method for HMCVT system efficiency was introduced. According to the shift strategy, the HMCVT physical prototype was tested under 10 working conditions and the traction operation of the tractor was simulated. The results of bench test indicated that the transmission efficiency of the system was higher than 0.85, except working conditions 1 and 2. Comparing with the bench test results, the simulation reflected the steady state characteristics of the HMCVT, efficiency error was about 4.7% under the minimum displacement ratio condition, and errors were less than 2% under other conditions. This method can have guiding significance in the stage of engineering research and development.

Abstract:The traditional multi-machine collaborative path planning method exists several problems which ignore the autonomy of the slave and the turning path may overlap on the ground. A multi-machine cooperative navigation path planning method oriented to master-slave cooperation mode was proposed. Firstly, the safety state detection model of agricultural machinery was established based on the directional bounding box algorithm and the separation axis theorem. By comparing the relationship between the sum of the projection radius of each agricultural machinery envelope section on the separation axis and the projection length from the geometric center of the section, the collision between agricultural machinery can be detected. Then, the host path planning model based on the full coverage algorithm was established, and the optimal operation direction angle was solved with the minimum turning number as the optimization objective; according to the size relationship between the minimum turning radius and operation width of the main engine, two kinds of U-shaped and T-shaped turning models were established. Finally, the path planning model of slave was established, and the linear operation path of slave machine was planned according to the relative distance between master and slave in cooperative operation; The cooperative turning modes of master and slave were divided into three types: double U-type, double-T-type and UT type. The cooperative turning strategy was proposed, according to the turning state of the master and slave, the waiting time of the slave was determined to avoid the collision risk caused by overlapping turning paths. Taking the wheat harvest scene under a convex polygon plot as the experimental sample plot, the longitude and latitude coordinates of the plot vertex were extracted by using LocaSpace Viewer, and the rectangular coordinates of the plot were obtained through coordinate transformation. The simulation experiment was carried out by using Matlab. The experimental results showed that the proposed method can plan multi-machine collaborative operation path with high land coverage, short operation time and low power consumption; the master and slave can turn successively according to the designed cooperative turning strategy to avoid collision, when the turning path overlaped; the minimum, maximum and average time of path planning algorithm were 0.453s, 1.563s and 0.951s, respectively. The proposed method can avoid collision risk and can provide an effective global operation path for wheat and silage harvesting.

Abstract:In order to realize the remote dispatching management of multi-machine cooperative navigation operation in complex farmland environment, the research of global path planning based on improved A-star algorithm and Bezier curve was carried out. Multi-machine cooperative operation path planning in farmland operation environment was introduced, which was divided into global path planning and local dynamic obstacle avoidance; The improved A-star algorithm was used to optimize the global path and corner optimization, and the global path was smoothed based on Bezier curve; According to the randomly generated obstacle environment map and Zhuozhou Experimental Farm environment map, the multi-machine cooperative global path planning algorithm was simulated by using Matlab platform. The simulation results showed that by adjusting the value of weight w(n) in the improved A-star algorithm, the search efficiency was significantly improved, the running time of the optimized algorithm was 0.832s in the simulation experiment based on Zhuozhou Experimental Farm. Through corner optimization, the number of turns was effectively reduced when the path length was similar. Similarly, after using Bezier curve to smooth the path, the peak at the corner was well optimized to ensure the smooth progress of agricultural machinery in the actual farmland operation, which preliminarily met the real-time and smooth requirements, and provided a basis for further solving the multi-machine cooperative path planning in the field environment.