Abstract:In the field operation of agricultural machinery automatic navigation system, the windbreak trees of field edge will have strong disturbance to the satellite signal. Because of the requirement of accuracy for agricultural machinery navigation and the general automatic navigation system has poor resistance to environmental interference, the algorithm of heading information fusion was studied based on the integrated navigation system of INS/GNSS. This algorithm adopted adaptive Kalman filter to reduced the noise with the measurement of heading data for single antenna GNSS and obtained the error estimation of heading data by using compensated Kalman filter. According to the quality of GNSS signal and the heading angle gradient, it could also reasonably allocate the weights of different fusion data by the calculation of federated filter. The results of simulation experiment and actual application test showed that: taking the heading measured value of double antenna GNSS as the reference data, the average absolute error of fusion heading data was -0.02° and the standard deviation was 0.50° in the process of linear driving. In the process of steering driving, the average absolute error of fusion heading data was 0.62° and the standard deviation was 2.42°. The accuracy of heading output after fusion had an obvious improvement when compared with using INS or GNSS separately. The noise of heading measured value of GNSS was filtered from the output of fusion heading and the update rate of GNSS calculated value was increased at the same time. The algorithm of heading information fusion could enhance the accuracy of the automatic navigation system for agricultural machinery and give full play to the advantages of the INS/GNSS integrated navigation system.

Abstract:In order to achieve the automatic navigation of corn weeding-cultivating operations and improve the efficiency and accuracy of automatic weeding, a corn weeding-cultivating integrated navigation system based on GNSS and MV was designed and developed. The system, which consisted of two parts, the agricultural vehicle automatic navigation based on GNSS and the weeding implement navigation based on machine vision, could achieve automatic navigation in the process of corn weeding-cultivating. In order to avoid the damage corn, the proper safety distance between implement moldboard and crop rows is necessary when the agricultural machinery is working, which is controlled by tracing the GNSS information of corn planting navigation and the image information of crop rows acquired by industrial camera. During the hardware part, the steering wheel controlling and the front wheel angle testing parts were designed and refitted. The steering control circuit and the implement moldboard hydraulic control circuit were designed based on PLC and stepper motor drive. To the vehicle navigation, the steering wheel controlling and the front wheel angle testing institutions were designed. The vehicle operated through tracing the corn row by the GNSS information of corn planting navigation. In the integrated navigation intelligent decision module, the fuzzy control system was taken as the main control algorithm for navigation control decisions, and the lateral deviation of agricultural machinery and lateral deviation error rate were used as input variables of fuzzy control. In the implement visual navigation module, the crop row detection algorithm based on scanning filter was adopted, which could improve the precision of navigation line detection and processing efficiency.Corresponding experiments were designed to test the feasibility of the system，the accuracy of automatic navigation were calculated respectively. The results show that, in the speed of 0.6 m/s, the maximum lateral deviation of GNSS navigation is 10.04 cm, the average deviation is 4.62 cm. The maximum lateral deviation of integrated navigation is 6.35 cm, the average deviation is 2.73 cm. The corn weeding-cultivating integrated navigation system can effectively satisfy the requirement of the corn weeding.

Abstract:John Deere AutoTrac automatic navigation system is an advanced and cost-effective navigation system. But this kind of systems is designed for John Deere’s tractor, it is not suitable for general vehicles. This paper proposes a mechanical modifications method based on XUV825i utility vehicle to solve the problem that ATU (AutoTrac universal) steering wheel can not be installed at utility vehicle. The mechanical modification method that installing a special joints between utility vehicle steering axis and ATU steering wheel can improve the concentricity, making utility vehicle under the control of AutoTrac automatic navigation system with high navigation precision. XUV825i utility vehicle’s structure is as same as general vehicles so that the mechanical modification method can also be adapted to the general vehicle. Finally, an navigation tests were carried out based on the modified utility vehicle. During the experiment, the path under manual driving was as same as the path under AutoTrac automatic navigation’s control driving. The average lateral deviation of the machine’s navigation path was less than 2 cm when the navigation system had been tested well and was working under the good road condition. This indicaties that the mechanical modification method is feasible and it does not affect the navigation accuracy of the AutoTrac automatic navigation system. At the same time, it shows that the mechanical modification method is applicable to general vehicles, that means, AutoTrac automatic navigation system can be used on general vehicles.

Abstract:Agricultural implement automatic navigation is a major trend of modern agriculture. Agricultural implement navigation can avoid the errors during implement shaking in field and improve the precision of navigation and flexibility of operation. At present, most of the navigation terminals were developed based on the industrial computer which is expensive and difficult to be spread. In this paper, an agricultural implement visual navigation terminal based on DSP and MCU for automatic weeding was designed. As the core processor of the system, DSP was responsible for the image acquisition, crop rows detection and offset calculation of navigation line. MCU is the main control unit of the system, so it was used to manage the working process, receive, store and forward GNSS data, and also control the implement. The corresponding protocol of serial communication, network communication and CAN bus communication in the system was normalized to make sure the stability of the communication. In the image processing, the OTSU method and the crop row detection algorithm based on boundary detection and scan-filter (BDSF) were adopted, which could improve the accuracy and efficiency of navigation line detection. In order to verify the validity and stability of the system, the algorithm adaptive test, offset accuracy test and different system comparison tests were carried out. The experiment results showed that the crop line detection algorithm could work adaptively in the weed and crop thinning conditions. The average error of offset detection is 1.29 cm and the maximum error is 4.1 cm. The systems comparison test verified the economic feasibility of the proposed system by compared with the PC and ARM, which can satisfy the requirements of filed operation.

Abstract:There are large differences between the actual colour of fruit trees canopy and the point cloud from the ground three-dimensional laser scanner in the complex outdoors environment. In order to solve this problem,a new color correction method for color correction was proposed. Firstly, the Pearson coefficient and Spearman correlation coefficient were calculated to make sure that there are relationships among the R,G,B values of the scanning spot and the R,G,B values of the 24-Patch Color Checker Chart Classic, the solar radiation values, the angle θ between 24-Patch Color Checker Chart Classic and the earth, scan quality, and light direction variables. And then, with confidence level of 95%, an R,G,B components multiple regression model was established by using the dual-sifting stepwise regression in statistical method. Lastly, the model was used to correct three-dimensional points cloud colors. The experiment results show that, using the three-dimensional point correlation regression model, the correlation coefficient between the three-dimensional point cloud color R,G,B value and the real R,G,B values increased from less than 0.69 to above 0.90 after correction,and color-corrected standard deviation fell significantly from above 50% to below 13%. The method could be used to provide a theoretical basis for terrestrial laser scanning to obtain more accurate three-dimensional color points cloud.

Abstract:In order to provide a scientific and reliable technical guidance for fruit harvesting robot in orchard, a method was proposed in this paper to reconstruct 3D image for apple tree and carry out recognition and location for each apple fruit. Firstly, the color image and depth image of the fruit trees were taken by an RGB-D camera, and the 3D reconstruction of each fruit tree was carried out by fusing its color and depth information. Then, 3D point cloud of the fruit region were segmented from tree’s point cloud by applying the color threshold of R-G . Finally, the 3D shape of each fruit point cloud was extracted and its 3D spatial position information and radius were also obtained by using iteratively the RANSAC(Random sample consensus) algorithm to fit each fruit to a pre-defined apple model.The experimental results showed that the proposed method of 3D reconstruction of apple tree and recognition and location of its fruits had good real-time performance and strong robustness. In the measurement range of 0.8~2.0 m,the correct recognition rates of fruits under frontlighting and backlighting conditions were 95.5% and 88.5% respectively, and the correct recognition rate was 87.4% in the case that the sheltered area of fruit point clouds was over 50%, besides, the average position calculation error of the fruit was 8.1 mm, and the average radius calculation error was 4.5 mm.

Abstract:Pruning is one of the important measures for fruit trees management. Different pruning methods have different effects on the growth of fruit trees. Research on the law of apple tree growth after pruning is of great significance to guide production. Apple tree，as a perennial，is quite complex in observation，modeling and calibration，which makes researches related quite a few. In order to study more reasonable pruning technology，judgment of pruning reaction needs to ensure the accuracy，intuition and timeliness and the simulation technology makes it possible. In this thesis，apple tree was taken as the research object，the data which was measured from the field experiment was analyzed，the law of pruning reaction was summarized and the structure model and pruning model of apple tree were built. The simulation software of apple tree pruning was established based on the Qt framework and OpenGL graphics library. For a given production target，the simulation software of apple tree pruning provides an optimized solution of pruning through the optimal algorithm.

Abstract:The quality of corn seed production and new variety breeding are affected by the problem of abnormal corn ears. Taking the whole corn ear as research object, the sorting method of three abnormal grains (namely moldy corn ears, worm-eaten corn ears and mechanically damaged corn ears) was researched based on two-dimensional fast imaging technology. Firstly, the portable image acquisition device was constructed based on the monocular vision and the corn ear image was acquired. According to these characteristics of corn ear images, six color features in RGB model and HIS model and five texture features in gray scale images were extracted and normalized to build the classification model of these abnormal corn ears. The classifiers were trained with the support vector machine (SVM) and BP neural network for comparison analysis by using the known feature vectors. The result showed that the SVM classifier had higher accuracy than BP neural network classifier. The accuracies of moldy corn ears sorting, worm-eaten corn ears sorting and mechanically damaged corn ears sorting were 96.0%, 93.3% and 90.0%, respectively. The study made an important foundation for realizing the automatic machine screening of abnormal corn ears and had high application value in improving the corn seed quality.

Abstract:During the whole crop growing period and high-precision breeding process, measuring crop plant type parameters to achieve its phenotypic analysis is one of the important link. In view of the problem that the maize plant type parameters are obtained mainly by artificial field measurement in China at present, and it is high labor intensity and time-consuming. Thus a rapid and nondestructive measurement method of maize plant type parameters was proposed based on the photonics mixer device (PMD) camera with improved skeleton extraction algorithm. Firstly, the RGB pseudo color depth and distance information of depth image were used, and the depth of the image skeleton extraction was obtained by the improved skeleton extraction algorithm without complex background interference. Secondly, the binary skeleton image per corn plant was got by taking advantage of the improved corner detection classification algorithm for extracting skeleton image feature points. Finally, the feature points in the skeleton image were corresponded with depth images. Three kinds of corn plant type parameters: plant height, stem diameter and leaf angle were calculated by using mathematics method combined with space geometry feature point. Field test of the method in the practical application environment showed that the plant height could be measured at the seedling stage of maize plant, the correlation coefficient ( r ) of maize plant type parameters between the measured results by using the proposed method and the results measured artificially was 0.986, and the maximum relative error was less than 2 cm. Farmland crops breeding resistance analysis also showed that maize plant type parameters and lodging resistance had significant correlation. The results provide technical support for the inversion of phenotypic analysis of crop breeding.

Abstract:The land precision leveling technology could improve farmland micro-topography and distribution uniformity of irrigation water and soil nutrients, save water and fertilizer and increase both production and income. To improve the working efficiency of GNSS-controlled land leveling system, a method of farmland regional planning based on spatial clustering was explored. Firstly, a topography contour map was generated by using terrain rapid measurement method. Farmland altitude data was obtained by the topography map. Secondly, the altitude data was divided into different categories by using K-means clustering algorithm, and the data in the space was marked by each category. Thirdly, the discrete marked data belonged to each category in the space was merged and error points were removed by using density clustering algorithm. According to the principle of land leveling, regional division was finished by adjusting the amount of digging earth and the size of the farmland area. Finally, farmland experiments were carried out and the results showed that the proposed method could be used to guide the driving well in the process of land leveling. The sum proportion of overload and empty load during the time when the land was planning and navigation functions were working was no more than 5%, which was better than the situation without these functions. The condition of farmland terrain under each region was improved significantly after being leveled. The value of the standard deviation of altitude was less than 6 cm and it could meet the preliminary goal.

Abstract:Kinematics model of mechanical hydraulic system usually involves the speed, acceleration and geometric constraint, which will contain one or two and even higher order differential equations. The number of differential-algebraic equations (DAEs) increases with the increase of system complexity. In order to find a suitable control arithmetic, it is needed to figure out the relationship of different state variables. However, it’s always impossible to get analytical solution. Thus it is needed to find the numerical solution of DAEs system, especially when it has too many state variables. Solving this problem with computer software is a common way; there are several helpful softwares, such as Matlab, Maple, Simulink and Mathematica. The mathematical function provided by the Matlab ode45 was used to solve the kinematics model of leveling system of paddy field leveler with sinusoidal input current. Firstly, the real paddy field leveler was simplified to kinematics model and showed in DAEs form based on theoretical mechanics and hydraulic theory. Secondly, the DAEs were changed into ODEs (ordinary differential equations). Finally, the ode45 was used to get numerical solution and show the relationship of state variables. The input current and output state variables were showed in figure. The method can help to get accurate numerical solution for DAEs system, and it also can display the relationship between the random input with known equations and other state variables. It will help to forecast the state variables at the next moment and design an efficient control algorithm for paddy field leveler.

Abstract:In order to improve the automatic operation degree in traditional peanut combined harvester, the online conditions measurement method for key working parts, such as picking roller, sorting screen, clamping shaft, clamping shaft and rotating components, was designed on the basis of 4HBLZ-2 one-row small self-propelled peanut combined harvester. Using LabView, an on-line operation monitoring system of peanut combined harvester was developed, with which the machine control state, harvesting operation mode, engine parameter, harvesting trajectory and working conditions of main parts could be real-timely monitored. The self-diagnosis and fault early warning model was established with the application of multi-sensor information fusion algorithm, which could offer alarm message for drivers under abnormal conditions, such as blocking of picking roller and chain break. Tests result showed that the time of automatic fault diagnosis was less than 2 min and the accuracy of fault detection was up to 90%. The designed system had met the functional requirements and precision needs of peanut combined harvester real-time monitoring in field operation.

Abstract:Aimed at the questions caused by unsuitable sampling frequency, such as data redundancy, high cost in hardware and software produced by high sampling frequency and the accuracy and stability which are hardly ensured under the condition of low sampling frequency, the grain impact frequency and sampling theorem were considered, consequently, the maximum sampling frequency was determined at 50 Hz. In data preprocessing, for high frequency sampling signal, two methods were put forward, one was an arithmetic average method, and the other was double threshold filtering mean method. The analysis result showed that the effect of double threshold value filtering and average treatment was better than that of the former. In order to investigate the influence of sampling frequency on yield estimation, the yield estimation tests of different sampling frequencies were carried out, and a frequency extraction method was proposed to generate different sampling signals. Under four kinds of sampling frequency as 1, 10， 25 and 50 Hz, four types of yield estimation models were established, and the prediction effects were also compared. The test results showed that high accuracy of the estimated yield can be obtained with high sampling frequency; using 50 Hz sampling frequency, the lowest average relative error was 3.04%; using sampling frequency higher than 10 Hz, it could ensure the average relative error was not higher than 5%. As a result, it is necessary to adopt at least 10 Hz as the sampling frequency for estimation of grain yield.

Abstract:pH value is one of the important parameters for evaluating the silage quality. A wireless pH value monitor device for silage was developed, and it could obtain the pH value of silage at real-time during fermentation. After the calibration of the device, the verification experiments of the actual measurement results were carried out in chopped maize and grass samples. The results 〖JP3〗showed that there is the linear correlation between the measurement result and the accurate pH value, r is 0.997 3 and 0.995 7 (chopped maize, grass sample, pespectirely). The experiments were carried out for monitoring the change of pH value during fermentation in chopped maize and grass silage respectively. The results showed that the original pH value in chopped maize is 5.4, and then the pH value decreased sharply from 5.4 to 4.0 between 0.5~2 d. After that, the pH value decreased slowly and became stable at 3.8 finally; the original pH value in grass is 5.9, and the pH value decreased from 6.0 to 5.2 between 0.8~3 d. Finally, the pH value became stable at 5.1 in 7 d fermentation.

Abstract:A modified screen-printed carbon electrode was prepared and manufactured by using ionic liquid, nano-Fe 3O 4 and in situ plating bismuth film methods, which were used to detect heavy metal ions of cadmium and lead by stripping square wave voltammetry. Some electrochemical methods such as cyclic voltammetry, electrochemical impedance spectroscopy and square wave stripping voltammetry were further applied to investigate the properties of this electrode. It illustrated that the formed bismuth film/ionic liquid/nano-Fe 3O 4 layer on the top of screen-printed carbon electrode could remarkably improve the electron transfer and the specific surface area of the electrode owing to ionic conductivity and adhesiveness of IL and high electron transfer of nano-Fe 3O 4. Under the optimum conditions, the linear detection range of modified electrode was 0.2 ~ 35.0 μg/L and 0.2 ~ 20.0 μg/L for cadimium and lead with detection limit of 0.06 μg/L and 0.1 μg/L ( S/N =3). Finally, the proposed analytical procedure was applied to detect trace metal ions in real samples with satisfactory selectivity and results.

Abstract:Fast and efficient access to the information of farmland is the basis of precision agriculture. Soil is an important part of agriculture for crop growth. Soil electrical conductivity is used for measuring the capability of soil conduct current. In addition to soil texture, soil electrical conductivity is also capable of reflecting the properties of moisture content, salinity, organic content, etc. Soil spectral data can be used for analyzing soil moisture, nutrient content, etc., without sampling or stirring the soil. The correction of soil electrical conductivity and spectral data can improve the accuracy of the system. Based on the embedded technology, a soil electrical conductivity and soil spectral reflectance integrated detection system was developed. Based on the improved “current-voltage four terminal” principle, the soil electrical conductivity system used subsoiling plow as electrode sensor. It could conduct measurement and loose soil as well. STS-NIR miniature spectrometers were used for acquiring the real-time spectral reflectance data. GPS information was acquired and saved synchronously with soil electrical conductivity data and spectral reflectance data for soil characteristics distribution. The system can handle a variety of data real-timely, and display, save data synchronously, and it has good application prospects.

Abstract:The conventional method of soil nitrate-nitrogen prediction based on ion-selective electrode had the problem of complex soil suspension components and the limited prediction accuracy and precision in single input variables. To improve the prediction accuracy and precision of soil NO - 3-N concentration employing ion-selective electrodes (ISEs), the support vector machine (SVM) model of soil NO - 3-N prediction based on sensor fusion was built. Grey relational analysis was applied to screen the major interference factors, which had a great impact on the soil NO - 3-N detection employing ISEs, and the support vector machine (SVM) model based on sensor fusion was built with the major factors. Then, the classical Nernst model and the SVM model with major factors and all considered factors were compared with the conventional method. According to the testing results, EC values, temperature and Cl - were the three major interference factors which had great influence on the prediction accuracy and precision of soil NO - 3-N concentration employing ISEs. With the optimized input parameters of NO - 3-N ISE potentials, EC, temperature and Cl - ISE potentials, the adjusted R 2 , average absolute error and root mean square error of the SVM model were 0.98, 3.38 mg/L and 4.51 mg/L, respectively. The SVM model based on sensor fusion showed more advantages than the Nernst model and it could successfully achieve the prediction purpose of NO - 3-N with high prediction accuracy and precision of the ISEs in soil extracted solution.

Abstract:The aim of the research was to improve the efficiency of soil pretreatment for ion-selective electrode (ISE) based soil nitrate-nitrogen detection. Modern experimental apparatuses for soil sample pretreatment, such as microwave, high-speed centrifuge and high-speed vortex oscillator, were validated to replace the traditional pretreatment tools. The single factor test was conducted. Four influencing factors, including microwave time, shaking time, centrifugation rate and time, were selected and optimized. The analysis of variance was applied to analyze the results of orthogonal experiments. The optimized parameters of ISE based soil rapid pretreatment process were obtained as microwave drying time of 9 min, high-speed vortex time of 40 s, centrifugal speed of 1 000 r/min and centrifugal time of 60 s. With the optimized parameters，the validation experiment of 59 soil samples indicated that the mean relative error of the rapid pretreatment and root mean square error were 7.48% and 7.91 mg/L, respectively. The rapid pretreatment time was less than 15 min for a sample.

Abstract:It was necessary to monitor liquid level in both mixing fertilizer tank and recycle tank respectively to design the integrating equipment of water and fertilizer in the intelligent hydroponic control system for facility vegetable production. In practical engineering project, the liquid level floater is widely used for the existing liquid level detection. The effect of the floater can be disturbed by some conditions, such as waterlines fluctuation when the floater was too close to inlet or outlet. Shaking up and down of the floater result in pump to start and stop intermittently, which will damage the pump with great starting current. To solve these problems, the counter was used to design digital filter circuit and MCU timer interrupt respectively in the hardware and software. In order to test the performance of the new intelligent liquid level switcher, the comparison experiments of different depths, pressures and water flows were carried out under the same conditions. The results show that this method can effectively filter out interfering signal. When both the charging resistance and discharge resistance of the counting pulse generating circuit are 4.7 kΩ, the filtered signal delayed 2.11 s after the fact output signal. The effective output signal was cleared off disturbing signal under 2 s by digital filter circuit and kept high stability. The delay time is related to the counting and pulse generating circuit, so different disturbing signals can be declined by configuring filter plus width in circuit design. On the way of software design, timer programming method was used in microcontroller considering width of the wave curve to detect the status of ports at certain intervals. The algorithm was introduced to remove disturbance and improve the efficiency of SCM. The method can be used to filter out the interference signal by setting different values according to the actual situation, such as different power pumps so the smart switcher can be more feasible.

Abstract:In order to improve the screening effect of fresh tea leaves, an equant-diameter roller screening machine was developed with adjustable structural and technical parameters. The main parameters of roller rotating speed, roller diameter and roller length were calculated, and the model selection of driving motor was done. The optimal parameters combination of roller inclination, rotating speed and feeding rate was made based on the orthogonal test. The treatments were arranged according to orthogonal table L 25 (5 6 ), and the influence of each parameter on screening rate was analyzed. The results showed that the sequence of each parameter’s influence on total screening rate was roller inclination, 〖JP3〗feeding rate and rotating speed, and screening rate reached the maximum when roller inclination was 6°, feeding rate was 1.5 kg/min and rotation rate was 16 r/min. To double the screening productivity, feeding rate should be selected as 3.0 kg/min, while correspondingly total screening rate was dropped by only 3.3% averagely.

Abstract:Abstract: Near infrared spectroscopy is currently a highly versatile tool used in diverse fields. However, outlier samples strongly affect the performance of the prediction model in near infrared spectroscopy analysis. Therefore, detecting and eliminating the outlier samples is a major and important procedure in near infrared spectroscopy analysis. Using the outlier samples detection based on joint X-Y distances (ODXY) method, a special outlier samples detection method for multicomponent analysis was proposed and proved, termed as MODXY method. Experimental data was derived from the near infrared spectra of 80 corns. Based on these, the PLS models of moisture content, oil content, protein content and starch content were constructed by eliminating outlier samples using different outlier detection methods. The obtained models were compared in terms of performance by the predictive root mean square error (RMSEP) and the coefficient of determination ( R 2). The results showed that in most cases the MODXY method had better outlier sample recognition capability in NIR multicomponent analysis compared with other methods. Both ODXY method and MODXY method had their own suitable range, and they were more effective when the relative standard deviation of components was large enough.

Abstract:Walnut is an important dry fruit and woody oil crop in China, and it has significant meaning to establish a rapid, nondestructive testing method for identification and classification of walnut kernel varieties in walnut processing industry. Near-infrared diffuse reflection spectroscopies of 200 walnut samples of four species were adopted to establish models for rapid and nondestructive classification. The spectral region of walnut samples was ranged from 3 800 cm -1 to 9 600 cm -1 . The spectra data of walnut were processed using the multiplicative scatter correction (MSC) and the standard normalized variate (SNV) methods. Principal component analysis (PCA) was used to reduce the dimensionality of spectra data. The cumulative contribution rate of the first five main components reached 99.21%, which were selected as variables for modeling. Totally 100 walnut samples were selected as training set by random sampling method. The NIR classification model of walnut kernel varieties was built based on support vector machine (SVM) method, and grid search method was used for searching the best parameter. The built model was tested by the rest 100 walnut samples of four species, and the results showed that the correct recognition rate of the model reached 96%. The analyzed results indicated that the NIR classification model could provide a feasible method for rapid and nondestructive identification of walnut kernel varieties.

Abstract:The internal quality of watermelon is closely related to its vibration characteristics. As a representative of the non-contact vibration method, laser Doppler vibrometry technology can accurately measure the real vibration of agricultural tissue, so as to obtain the information of internal quality of the agricultural products. In this paper, the influence of vibration parameters on the watermelon vibration frequency response characteristics was firstly studied based on single factor experiments with the amplitude of acceleration, the frequency sweep rate and testing point of watermelon. Then, a interaction of multi-factor orthogonal experiment was conducted, which was performed by the laser Doppler vibrometry system based on the above factors and repeated three times at each parameter combination. The results of the single factor experiment indicated that the amplitude of acceleration and frequency sweep rate had significant effects on frequency spectrums, but the effect of testing point was not significant. The results of interaction of multi-factor orthogonal experiment showed that the optimal combination of the amplitude of acceleration, the frequency sweep rate and testing point for watermelon vibration measurement was 2.5 g , 1 000 Hz/min and the sunny side of the equator. The results laid a foundation for accurate nondestructive detection of internal quality for watermelon.

Abstract:The head cabbage has three types according to its external ball shape, i.e., tip, flat and round shape types. The traditional identification method of cabbage ball shape is done artificially. A new method for rapid identification of cabbage ball shape was proposed using machine vision technology combined with BP neural network. Firstly, four absolute cabbage shape parameters were extracted, such as height, width, long axis and area, based on image processing technology. Five relative shape parameters were defined based on the above absolute parameters, which were ratio of height to width, circular degree, rectangle degree, ellipse degree and dome shape index. These nine parameters were used to describe the cabbage shape. Since the parameter ranges overlapped, the individual parameter did not have separating classification ability. Secondly, three recognition models of cabbage ball shape with BP neural network were established using three types of input datasets, four absolute parameters (long axis, height, width, area), five relative parameters (ratio of height to width, circular degree, rectangle degree, ellipse degree, dome shape index) and all above nine parameters. Each network had ten neurons in implicit layer, three neurons in output layer. Scaled conjugate gradient algorithm was used to train the network. The test results showed that the prediction accuracy of BP neural network model took four absolute parameters as the input was 62.5%, and the prediction accuracies of other two models were 100%. The model with relative parameters was relatively small and simple, and could shorten the time of network computing. Meanwhile, the center distance values of every two type training sample groups were computed, and the result showed that the model with all nine parameters had the biggest distance, which made the network be adapted to a wider sample spherical recognition.

Abstract:Abstract: In order to realize the automatic harvesting of sea cucumber and diagnose the disease of sea cucumber, first, the problem of the image segmentation of sea cucumber under real aquaculture environment should be solved. In this paper, a new method of image segmentation of sea cucumber using GrabCut with saliency map was proposed. This method improved the traditional GrabCut algorithm, enhanced underwater images through the single scale Retinex algorithm. Based on global contrast based salient region detection method and histogram equalization, part of foreground and possible background of regional image of sea cucumber could be obtained, the mask of GrabCut algorithm can be initialized using this information. At last, GrabCut algorithm ran iterated to get the result of image segmentation. Experiment results proved that the proposed method can segment the sea cucumber images more accurately than the Otsu method, the watershed method and the traditional GrabCut algorithm, and overcome the background noise and preserve the details of the target image. The accuracy of the algorithm was 90.13%.

Abstract:Abstract: In internet of things in farmlands, control commands are usually triggered by complex events which contain many dimensionalities of information. Complex events are detected by combining mass of atomic events directly sensed by sensors. That is a typical procedure of complex event processing (CEP). As a basis of CEP, a model of complex events, which describes how atomic events construct complex events, is necessary. Current complex event models focus on temporal logic, but spatial logic relations among farmland events are not considered. In this paper, a novel model that considers both temporal logic and spatial logic was proposed and a relevant XML-based specification language was designed. In the model, multi-level complex events were modeled to a directed graph. For one complex event in the graph, based on analysis of temporal and spatial logic relations among component events, nine time relation operators and eight spatial relation operators were defined to describe above relations; five time combination operators and seven spatial combination operators were defined to calculate attributes of complex events from attributes of atomic events. Based on the model, a XML-based language was defined, which balances readability of users, descriptive abilities and difficulties of parsing and compiling. The proposed event model and language were compared to those used in popular CEP systems e.g. SASE+, Esper, etc. From the comparison, the proposed model and language were more suitable for describing complex events in internet of things in farmlands.

Abstract:Data fusion for wireless sensor networks (WSN) can reduce the energy consumption of sensor nodes and prolong the network lifetime, so that it has attracted wide spread attention in a variety of applications. The existing algorithms for spatial data fusion that have been used in agricultural monitoring always aggregate the data within a certain area into one value by means of averaging. However, in addition to redundancy resulted from correlation, the sensed data also has variance due to larger monitoring area, more monitoring nodes and larger amount of data in farmland environment. Hence, data fusion in farmland monitoring should retain the differences of data while eliminating the redundancy. The idea that applying data fusion algorithm on WSN cluster head to aggregate spatially correlated data by clustering was proposed. While the parameters whose values are quite different will be clustered into different categories so that differences between the data can be reserved. An improved adaptive K-means clustering algorithm was proposed to be used in cluster head. Simulation results indicate that, the amount of data uploaded with fusion algorithm was decreased by 41.19% compared with that without fusion algorithm,and the maximum error before and after the proposed fusion algorithm is less than 36% of that before and after the averaging fusion method.When there is no clear accuracy requirement,the proposed algorithm can reduce the amount of data uploaded and maintain the relative error less than 10%, 〖JP3〗avoiding enormous error caused by improper number of clusters.When there are specific accuracy requirements, the relative error produced by the proposed algorithm can meet the error requirements strictly.

Abstract:Dissolved oxygen, pH value, conductivity, temperature, etc. are key factors for analyzing water quality. And, how to measure the key factors real-timely is particularly important. It is impossible for traditional empirical methods and chemical detection to satisfy production requirements today. However, with the development of intelligent and networked sensors, wireless network technology can satisfy the requirements of accuracy and real-time in water quality monitoring. ZigBee technology not only has characteristics of short distance, low complexity, powerful networking capability, low cost and high stability, but also has self-owned wireless transmission standard in which multiple modes which can relay each other achieve effective communication in measuring nodes, which fully meets the needs of the wireless water quality monitoring. This paper puts forward wireless water quality monitoring system based on JN5139 ZigBee wireless module. The system gathers perception module, micro-control module and wireless transmission module. Multiplex water quality parameters are collected periodically by wireless network, stored and finally displayed on host computer. These data can also be checked by users after connecting JN5139-Z01-M02/4 system through computer. In case of buildings, trees and obstruction, distance accuracy is 100 m at least. According to experiments, the system has high scalability, low power consumption, strong stability and so on, which can meet the requirements of real-time and data accuracy in water quality monitoring.

Abstract:In order to provide personalized services for agricultural information recommendation, it was needed to organize and classify information efficiently. According to the characteristics of agricultural texts, a Chinese agricultural text classification model was proposed based on linear support vector machine (SVM). Firstly, an agriculture-domain-based dictionary was built. Secondly, a feature vector was extracted and the weight for each feature in a vector was selected. Lastly, a text classification model was established. The model was tested on 1 071 documents which were belonged to four classes: planting, forestry, animal husbandry and fisheries. The results showed that the accuracy was 96.5% and the recall rate was 96.4%. Both of their performances were higher than those of the ones using other classification methods, such as the Bayesian, decision tree, KNN, SMO algorithm and neural network. The model was applied to the platform for agricultural internet of things (IOT) industry integrated information service. The performance showed that the method can automatically classify Chinese agricultural text information and the response time met the system requirements.

Abstract:In order to solve the on-line detection of the body surface temperature for sow based on thermal infrared video, the image segmentation method for the fast and efficient target detection was proposed. The thermal infrared image of the sow has the features of low pixel, low contrast and edge blur. In piggery environment conditions, the sow body temperature and background radiance were the main factors to affect thermal infrared image brightness and handling results. Because of the strong correlation between the intensity of the background radiation and light intensity, in order to study the effect of background radiation on the thermal infrared image segmentation, the thermal infrared images of different illumination intensities were collected. Firstly, the point operation was used to enhance the contrast enhancement; and then, instead of a constant value for ω , a weight function that varies dynamically with the global and local contrast of the image was chosen, so as to dynamically balance the global energy and the local energy; finally, an improved LGIF model was established with the global fitting energy and the local energy. 300 thermal infrared images were collected by using infrared thermal imaging system, and the image segmentation experiments were performed. These pictures were taken in different positions, light conditions, and sow varieties. Classification tests were carried out under three conditions of low light intensity (100~600 lx), middle illumination (600~1 000 lx) and high illumination (1 500~2 500 lx). In order to analyze the accuracy and real-time performance of the algorithm, the average running time and the correct segmentation rate of different segmentation algorithms were calculated respectively. The cause of the poor effect of the partial sample was analyzed, and the direction of improvement was put forward. Experimental results show that the improved method can extract the sow more efficiently, and the average single image segmentation time was 49.67 s, the correct segmentation rate reached more than 98% which demonstrated the accuracy and superiority of the proposed model.

Abstract:Abstract: In order to deal with various background situations, the complex lighting situations and the goals-background-mixing situations in piggery, a new kind of tracking method which is based on traditional compressive tracking algorithm was proposed. Firstly, to reduce the tracking error, we changed the search window to oval which is closer to the pig body. Secondly, to increase the stability of feature extraction and reduce drift, we combined gray feature with texture feature, and improved the random measurement matrix of traditional compressive tracking algorithm. Lastly, the piggery was divided in different areas. Based on the location of the target pigs we can analyze and assess its current behavior. Test results of different video samples and tracking results show that this algorithm improves the accuracy significantly in the piggery scene. The mean value and the variance of central point error in the proposed method were 25.44, those were 60.32%，33.33%，32.57% of the mean value of central point error in the CT method, TUT method and Camshift method. The tracking rate and it reaches to 19.3 frame/s.

Abstract:Abstract: Light intensity is one of the indispensable factors for plant growth and transpiration. Real-time monitoring light intensity and guiding irrigation by it can stimulate plant growth, simultaneously, and play a role in saving water and energy. In this paper, a practical light intensity detection system was designed in a simple way with low-cost. The silicon solar panel was used as the solar radiation sensor, which could directly transfer the sun’s radiant energy into electric signals. The PIC16F876A MCU was used as the processor. The least square procedure was used to establish the model between the output voltage of silicon solar panel and the light intensity, then experiment was carried out to verify the performance of this system. The result showed that the average relative errors were around 1.19% in sunny days, around 1.57% in cloudy days, around 7.19% in rainy days, around 6.15% in changing weather, respectively. The average relative error was always below 10% in different weathers. The system accuracy was increasing while the light intensity increased. The system worked more precise when the light intensity was above 15 000 lx with the average error of 1.41%. The resolution was 0.1 lx. The system repeatability error was very low (<0.63%), which means the system was running in high stability. In summary, this system could work stably in the solar greenhouse in different weathers.

Abstract:Abstract: Carbon dioxide (CO 2) is an important raw material of the plant photosynthesis. Increasing CO 2 fertilizer rationally can improve the net photosynthetic rate of plant leaf, and further improve crop yield and quality. To achieve precision management of CO 2 fertilizer in greenhouse, a greenhouse CO 2 fertilizer optimizing control system based on wireless sensor network (WSN) was designed and developed. The whole system includes four monitoring and controlling nodes, an intelligent gateway and a remote management software. The monitoring and controlling node, which connected to sensors and an electromagnet, can real time monitor greenhouse environmental parameters and control the switch of CO 2 source according to the demand of crop. The intelligent gateway can process and transmit the data and commands between nodes and remote management software. It can also storage and display environment parameters locally. Besides, user can control the CO 2 source by gateway. The remote management software, which embeds photosynthetic rate prediction model, can not only process and transmit the data, but also control CO 2 fertilizer remotely. To achieve precision management of CO 2 fertilizer supplement, it was necessary to build an accurate and reliable net photosynthetic rate prediction model. The paper measured environment parameters by the system above mentioned, and obtained single-leaf net photosynthetic rate by LI-6400XT photosynthesis analyzer. Then a photosynthetic rate prediction model based on SVM was established. In order to improve the generality of prediction model, tomatoes in late seedling stage were cultivated in four different fertilizer levels ((700±50)μmol/mol (C1), (1 000±50)μmol/mol (C2), (1 300±50)μmol/mol (C3), ambient about 450 μmol/mol (CK)). The photosynthetic rate prediction model was established by support vector machine (SVM). The environment parameters were used as input variables, and the photosynthetic rate was taken as output variable. The performances of designed system and prediction model were evaluated. The system can work stably and reliably, therefore it can be used to monitor environment information and control the CO 2 fertilizer in solar greenhouse. The prediction results of the model showed that R between predicted and measured data was 0.981 5 and RMSE was 1.092 5 μmol/(m 2 ·s). According to the analysis, it was concluded that the prediction model can be good used as the basis of the quantitative regulation of CO 2 fertilization to tomato plants in greenhouse.

Abstract:Abstract: Photosynthesis is the basis of crop growth and metabolism. CO 2 concentration and soil moisture are the important environmental factors affecting plant’s photosynthetic rate under controlled temperature and light intensity in greenhouse. To effectively evaluate the effect on plant’s photosynthesis, reasonably elevating CO 2 concentration under different soil moisture conditions is of great significance to achieve precision regulation of CO 2 concentration. To achieve the requirements, the photosynthetic rate prediction models based on back-propagation (BP) neural network were proposed at different growth stages of tomato plants. The two-factors interaction experiment was designed, in which the CO 2 concentration was set to four different levels ((700±50) (C1), (1 000±50) (C2), (1 300±50) μmol/mol (C3), and ambient CO 2 concentration in greenhouse (450 μmol/mol, CK)) combined with three different soil moisture levels (35%~45% (low), 55%~65% (moderate), 75%~85% of saturated water content (high)). The sensor nodes of WSN were used to realize the real-time monitoring of greenhouse environmental factors, including air temperature and humidity, light intensity, CO 2 concentration and soil moisture. An LI-6400XT photosynthesis analyzer was used to measure net photosynthetic rate of tomato leaf. The environmental factors were used as input variables of models after processed by normalization, and the photosynthetic rate was taken as the output variable. The model verification test was conducted by comparing and analyzing the observed values and predicted data. The results indicated that the training determination coefficient (R 2) of photosynthesis prediction model was 0.953, and root mean square error (RMSE) was 1.019 μmol/(m 2 ·s); testing R 2 of the model was 0.925, RMSE was 1.224 μmol/( m 2 ·s ) at seedling stage. At flowering stage, the training R 2 of the model was 0.958 and RMSE was 0.939 μmol/(m 2 ·s); testing R 2 of the model was 0.920 and RMSE was 1.276 μmol/(m 2 ·s). At fruiting stage, the training R 2 of the model was 0.980 and RMSE was 0.439 μmol/(m 2 ·s); testing R 2 of the model was 0.958 and RMSE was 0.722 μmol/(m 2 ·s). It was concluded that the model based on BP neural network reached high accuracy. Furthermore, the relationship between CO 2 concentration and photosynthetic rate was described by the established BP neural network model aiming at CO 2 saturation points under different soil moisture conditions at different growth stages. The observed and predicted results showed the same trend. The results can provide a theoretical basis for quantitative regulation of CO 2 enrichments to tomato plants in greenhouse.

Abstract:In order to rapidly detect the chlorophyll content of winter wheat canopy leaves in the field, a vehicle-mounted multi-spectral imaging system with 2-CCD camera was developed, and the working performance of the system was analyzed at different vehicle speeds. The FOTON-4040 tractor was used as the vehicle platform equipped multi-spectral image intelligent sensing system. Four speeds were set up in field experiments, 〖JP3〗which were S1 (0.54 m/s), S2 (0.83 m/s), S3 (1.04 m/s) and S4 (1.72 m/s). Visible and near infrared canopy images of winter wheat were collected. Meanwhile, the GPS position information was obtained and the SPAD values which indicated the chlorophyll content of winter wheat leaves were measured. Multi-spectral images were processed by adaptive smoothing filtering and canopy segmentation. There were 10 parameters in the image detection. The average gray values of four bands ( R, G, B and NIR) were extracted, and four vegetation indices (NDVI, NDGI, RVI and DVI), mean value of H in HSI model and canopy cover degree C were calculated. The correlation between each parameter of the image and the SPAD value of the chlorophyll index was analyzed. The results showed that the correlations between the parameters of each image and the chlorophyll index at speed of S1, S2 and S3 were higher than that at speed of S4. The correlation coefficients between NDVI, RVI, NDGI and the SPAD value reached over 0.50 at speed of S1, S2 and S3. MLR models for the diagnosis of the chlorophyll content were established at different speeds of S1, S2 and S3, respectively. The model precision met the requirements of crop growing space distribution map. In order to further improve the diagnostic efficiency of the crops growth parameters in the field, the MLR model of the chlorophyll content in winter wheat leaves was built by NDVI, NDGI and RVI. The results showed that the model was universal. The research can provide support for the rapid diagnosis of field crop growth.

Abstract:In order to realize precision management of wheat crop, a spectral analyzer was developed to diagnose crop canopy nutrition, and the performance experiment was conducted in a wheat field. The system consisted of optical system, signal acquisition driver module and controller. The optical sensor could collect spectral reflectance between 300 nm and 1 100 nm. The signal acquisition driver module was used to provide a stable voltage and A/D conversion. A spectral acquisition and control software was developed to receive, display, process and save the collected data. Calibration experiments and field experiments were carried out in wheat experimental field. The correlation between the result measured by the spectral analyzer and the result of ASD FieldSpec HandHeld 2 were analyzed. The results indicated that high correlation existed between the reflectance datasets detected by the two equipments. The minimum coefficient of correlation was 0.991 8. The correlation between the winter wheat chlorophyll content and reflectance measured by instrument was analyzed. Selecting the higher correlation band at 550~900 nm, the principal component analysis was applied to establish chlorophyll forecasting model. The determination coefficient R 2 of the calibration model was 0.575 and the R 2 of the validation model was 0.595. Results show that the developed instrument can effectively detect and evaluate chlorophyll content of wheat canopy, and provide theoretical and technical support for the precision cultivation of wheat.

Abstract:In order to rapidly detect the nutrition content of crop, a portable multispectral detector was developed. The detector was composed of a controller and an optical sensor node, which communicated with each other through the ZigBee protocol. The optical sensor node can measure both the intensity of solar light and crop reflective light at 550 nm, 766 nm, 650 nm and 850 nm wavebands, respectively. The control unit is a PDA, in which a ZigBee wireless communication module is embedded. As the coordinator of the whole wireless sensor network, the ZigBee wireless communication module is responsible for receiving, processing and displaying the spectral data transmitted by the measuring unit. The objective of the research was to assess the agronomic performance of the detector, i.e., the accuracy of chlorophyll content estimation when using the instrument in different arable crops. Field experiments were conducted on four varieties of corn (Nongda No. 8 (G1), Zhengdan (G2), Xianyu (G3) and Jingnongke (G4)). Crop canopy reflectance was measured by the detector at jointing stage. The chlorophyll contents of sampling leaves were measured by the spectrophotometer in the laboratory. According to the typical spectral characteristics of crop and soil, the differences of reflectance between 550 nm and 650 nm ( T D ) were used to remove the soil background data points ( T D >0). Furthermore, combinations of R nir and R r ((850, 550), (850, 650), (766, 550) and (766, 650)) were used to calculate vegetation indices, including DVI, NDVI and RVI. Relationship between each vegetation index and chlorophyll content of each variety was analyzed. The results showed that the optimal parameters of G1~G4 were RVI (766, 550), DVI (850, 650), NDVI (850, 550) and RVI (766, 550), respectively, and the correlation coefficients were above 0.6. The chlorophyll content of the four varieties was clustered respectively at intervals of 0.2 mg/L, 0.5 mg/L and 0.8 mg/L. The correlation analysis results showed that the optimal resolution of the multispectral detector for detecting chlorophyll content of corn was 0.5 mg/L. The correlation coefficients of NDVI (850, 550), NDVI (766, 550) and RVI (850, 550) and chlorophyll content were 0.837 0, 0.773 7 and 0.767 7, respectively. The NDVI (850, 550) and RVI (850, 550) were selected to establish the diagnosis model with R 2 C of 0.715 4 and R 2 V of 0.684 0. The research could provide theoretical and technical support for the diagnosis of chlorophyll content of corn at jointing stage.

Abstract:Wheat is a main crop in China and the timely and accuracy estimation of wheat yield is significant. The number of wheater in certain area is an important element in wheat yield estimation. The counting method of wheatear based on machine vision technology was studied, which was cheap and suitable for local area. The method was very significant for wheat growth monitoring and yield estimation. Firstly, the counting method for wheatear in field based on machine vision technology was studied by collecting images of wheatear colony with cameras deployed in the field. The analysis method for wheatear image feature, the thinning method for wheat ear outline and wheatear counting method based on skeleton were realized. The low resolution images of wheat plant were collected with cameras deployed in field. Then the color features and texture features of images were extracted. The outline of wheatear was extracted to get binary image of wheatear by using learning method of SVM. The database of wheatear feature was constructed at the same time and wheatear skeletons were generated by thinning the wheatear binary image. Finally, the number of wheatears was calculated by calculating the number of skeletons and skeleton intersection points. The method was tested in Zhaohe Demonstration Area, Fangcheng County, in May of 2014 and 2015. As a result, it took averagely only 1.7 s to calculate the number of wheatears and the accuracy was 93.1%, which means the wheatear counting method presented meets the requirement of both speed and accuracy, and it can provide reliable data for wheat yield estimation.

Abstract:In order to rapidly acquire winter wheat growing information in the field, the retrieval method of vegetation coverage index(VCI) was researched based on multi-spectral imaging technique and imaging processing technology. Firstly, a 2-CCD multi-spectral image monitoring system was used to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (RGB) and near-infrared (NIR) band. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the canopy image of winter wheat was segmented. HSI color model and automated threshold method were used to segment the RGB and NIR image respectively. The hue threshold was ［ π/4 , 6π/5］. The segmented results of RGB and NIR were combined to improve the segmentation accuracy and the VCI was calculated. Thirdly, the image parameters were abstracted based on the original visible and NIR images including the average gray value of each channel( A R, A G, A B ) and near-infrared ( A NIR ),the vegetation indices (NDVI, NDGI, RVI, DVI) which were widely used in remote sensing, and the H average value of canopy. The correlation analysis results showed that the correlation coefficients between vegetation indices and VCI were above 0.90. As a result, the retrieving multiple linear regressions (MLR) model was built by using NDVI, NDGI, RVI and DVI with R 2 c =0.948 and R 2 v = 0.884. It was feasible to diagnose vegetation coverage in the field and indicate the growth status.

Abstract:Abstract: Getting all kinds of crop planting area information accurately is the Agricultural Information Management Department’s main responsibility in order to master the basis of crop production information in an efficient manner. A remote sensing classification method was used based on time-series NDVI that is gathered by using Landsat8 satellite equipped with remote sensing technology. This remote sensing technology possessed a short cycle, performed its analysis in a very speedy manner and used a strong microscope to closely analyze the area it has been assigned to. Based on the analysis of the time-series spectrum character curve, crop type identification and acreage extraction can be effectively achieved. This helped to overcome the confusing agricultural crops classification problem caused by “same object with different spectra” and “foreign body with spectrum” by using a single temporary remote sensing image. In order to accurately ascertain the planting area for the various kinds of crops for providing technical support, the best NDVI threshold range for the crops was studied and the various crop classification rules were explored. The Quzhou County, Hebei Province was taken as the study area, and a distribution map of the study area was made based on this information which was gathered in 2014. Throughout five time phases of Landsat satellite data gathered in 2014, a study on the classification of remote sensing for planting area of winter wheat, summer maize, spring corn, cotton, and millet in the study area was conducted. Classification results can be shown for 2014 with all kinds of crops in the study area, respectively: winter wheat is 27 776.61 hm 2, summer corn is 27 776.61 hm 2, spring corn is 2 582.73 hm 2, cotton is 6 485.94 hm 2, and millet is 277.65 hm 2. Using the Kappa coefficient and statistical data to verify the accuracy of this classification, the result shows that the winter wheat, summer corn, spring corn, cotton and millet can be effectively identified, with an overall classification accuracy of 89.166 7%, along with a Kappa coefficient of 0.857 4. Compared with the statistical data, the relative margin of error for individual crops is as follows: winter wheat -0.80%, summer corn -0.32%, spring corn -3.15%, cotton -2.71%, millet 4.12%. This paper proves that mass crop planting areas can be precisely obtained from analyzing the time-series data of remote sensing images with a medium spatial resolution. It also proves that this method can provide a technical basis for using remote sensing to investigate crop planting areas at a county level.

Abstract:Obtaining planted area of crop has important significance for guaranteeing nation grain safety. The Farm NO.597, located in Baoqing County, Shuangyashan City, Heilongjiang Province was selected as an example to extract rice and maize planted area by taking WFV (Wide field view) sensor carried on GF-1 satellite with the spatial resolution of 16 m as data source, using the image produced on October 30, 2014, and calculating different characteristic bands. Firstly, the multi-characteristic data set was established based on the NDVI (Normalized difference vegetation index) calculated from the source image and the first three principal components analyzed by PCA (Principal component transform). Then, using the difference between different surface features in each characteristic band, the decision tree was built based on CART (Classification and regression trees) to classify rice and maize. The results showed that the overall classification accuracy was 96.15% and the Kappa coefficient was 0.94. Producer accuracy of rice was 98.41% and user accuracy was 97.64%. Producer accuracy of maize was 95.38% and user accuracy was 97.89%. This method provides the reference value for crop type mapping using GF-1 data in other agricultural areas.

Abstract:The geometrical registration of remote sensing image is an important premise for the subsequent processing of image. And it’s also an important security for the application, such as agricultural condition monitoring. Different algorithms of automatic geometry registration lead to various registration effects. It’s hard to meet the registration requirements of all images. Four testing types of plains, mountains, summer and winter were selected based the features of terrain and time. The main three registration methods were: cross correlation algorithm based on region gray, mutual information algorithm based on region gray and SIFT algorithm based on features. SIFT feature is the partial feature of the image, which can keep the invariance in rotating, scale-zooming and brightness changing. Then the automatic geometric registration was made for four classes of GF-1 remote sensing image using the above three algorithms. Two kinds of experiments were conducted for GF-1 remote sensing image under various conditions such as different terrains and different imaging time. The comparison of different geometric registration algorithms were made in the aspects of accuracy, efficiency and stability. The results show that the SIFT algorithm is the most appropriate one. The visual edge effect is good and the root mean square error reaches the magnitude of 10 -5 , which can satisfy the demand of precision. This method is simple and efficient, and it can be applied into agricultural condition monitoring and other business efficiently.

Abstract:In the process of constructing an ecological environment evaluation system of remote sensing, due to the business-core development mode, there is high degree of coupling between a variety of business models and the system, and the model reused could be a difficult problem. The development platform restriction of the API library itself leads to the lack of invoking ability for multi-platform.At the mean time, under the background of remote sensing of big data calculation, it is more difficult for the system to cope with the problem that multi-user concurrent requests, long time delay which is caused by wide area coverage calculation and the high system resources occupied. In the view of above problems, the paper puts forward an ecological environment evaluation model library that based on SOA and OpenStack. In terms of model reused and multi-platform invoking problem, 20 kinds of commonly used remote sensing thematic evaluation algorithm models were unified packaging, deployed and concurrent tuned as Web services. To solve the problem of model concurrent processing and large data computing, it utilized OpenStack to solve dynamic load balancing and task allocation for multiple services. On the other side, the paper analyzed the practical problems of core metadata interface design and encapsulates during the process of building a model library, and then provided new design idea. At the end, it developed ecological environment production evaluation that based on Three-river Head Source of Qinghai Province as example, which proved a stable system operation results.

Abstract:The air temperature within near-ground layer is an important surrounding factor that can affect winter wheat growth. The simultaneous heat and water (SHAW) model, which is a detailed process model of heat and water movement in the plant-snow-residue-soil system, was evaluated in simulating the air temperature within near-ground layer from 0 cm to 40 cm at after-jointing stage of winter wheat. Field experiment was taken in Shangqiu City, Henan Province to observe the winter wheat growth and surrounding factors, such as air temperature. The SHAW model was calibrated and driven with inputs of part of field experiment data and empirical parameters. The results showed that the SHAW model performed well in simulating air temperature within near-ground layer in winter wheat field, with 48% of the absolute error of simulated values was less than 1℃, 75% of the absolute error of simulated values was less than 2℃，and the model efficiency at different heights was higher than 0.94. The simulated values had higher biases during the day than those at night and they were increased with the increase of height from ground, and their biases generally reached the largest value during 11:00 and 14:00. The daily mean values of the simulated and observed air temperature values were basically the same, while the daily lowest values were overestimated and the daily highest values were underestimated. The model had better effects at jointing, filling and dough stages than those at booting, blooming and heading stages.

Abstract:Traditional breeding evaluation methods focus on information of crop traits, while ignoring the previous evaluation results. In order to enhance the efficiency of material evaluation under the condition of large-scale breeding, the comprehensive evaluation of crop traits was integrated into the breeding evaluation, and a method of phenotype feature selection for crop breeding evaluation based on ranking relevance was proposed. Firstly, the training sample set and the candidate feature set were selected from breeding data, and the correlation between the phenotype feature and the results of evaluation and the similarity of the agronomic traits were calculated. Then, considering the characteristics of the maximum correlation coefficient and the minimum similarity, a model of phenotype feature selection for breeding evaluation based on ranking relevance was constructed. The established model can be used for different breeding objectives focusing on collection of characters, and validity of the model was verified by using three kinds of soybean identification trial in 2013. The model also can be used as the preprocess of breeding evaluation to determine the weights of the traits accurately.

Abstract:Statistics analyses of spatio-temporal land use data, such as historical review, flow analysis，change index analysis and trend analysis, are important land management operations, and attract more and more attention from management and planning department. To overcome the difficulty in statistics of annual land use type in any query region and the time consuming problem in long time series change flow analysis, the statistical optimization method based on spatio-temporal variation model was proposed. For the former difficulty, the feature entities in the statistical region and boundary were classified with the proposed method based on the principles of connectivity of graphics, and then the statistical optimization algorithm of sequential snapshots was used to realize the statistics of time point status in any query area. For the latter problem, the spatio-temporal network approximation judging was carried out with the method based on multi-commodity flow principle, to reduce time consuming and improve the efficiency of long time series change flow analysis through reducing the number of spatial overlay analysis. Finally, the effectiveness and feasibility of the proposed method were verified through case study using land use data of Qionghai City, Hainan Province from 2009 to 2012.

Abstract:The remote sensing images from GF-1 satellite in 2013 and 2014 were used to detect the land use changes of the coastal zone in Haiyan County, Zhejiang Province. Two detective methods were compared through the pixel-based and object-oriented changes. In the pixel-based land use change detection, the multi-band difference and ratio methods were used to detect the land use changes based on multi-spectral and fusion images. In the object-oriented land use change detection, the effect of multi-spectral and fusion images were researched using multi-band difference and ratio methods on the single-level and multi-level. On this basis, the detection results of human activities region combined with shape features were analyzed. In addition, the change vector analysis (CVA) was adopted to conduct land use change detection basing on the multi-spectral and fusion images. The results showed that the overall accuracy of object-oriented land use change detection was 86.29%, and Kappa coefficient was 0.72, which were better than those of the pixel-based land use change detection. In the object-oriented land use change detection, the detection results of multi-level multi-band difference and ratio methods which using fusion image were the best, and they were better than those of CVA. And in the pixel-based land use change detection, the results of multi-band difference method which used fusion image were better than those of the other methods.

Abstract:In order to achieve the maize sowing time decision-making and improve the effective accumulated temperature of maize growth, it is needed to understand the soil spatial variability characteristics. Totally 20 sets of wireless sensor network nodes were deployed in Zhaoguang Farm in Heilongjiang Province for one month in 2015. In addition, two handheld mobile sensor nodes were chosen to increase the measurement number. According to the method, soil temperature and moisture data were obtained from 5 th to 29 th in April with 240 m×240 m, 120 m×120 m, 60 m×60 m and 30 m×30 m grids. The isotropic and anisotropic variation characteristics and distribution patterns of soil temperature and moisture were analyzed based on statistics semivariance function theory and GIS space Kriging interpolation method. Experimental results showed that the semivariance of soil temperature and moisture were suitable for the spherical model and exponential model, respectively. Both of them had strong spatial autocorrelation. The distribution of soil temperature was block with autocorrelation distance of 51.56 m. And the distribution of soil moisture was ribbon with autocorrelation distance of 154.16 m. The anisotropy of soil temperature and moisture variation was also significant. The soil temperature variations in 45° and 90° directions were significantly greater than those in 0° and 135° directions. The soil moisture determination coefficient ( R 2 ) was 0.77 with significant variations in 0° and 135° directions. The research results provided a scientific guidance for the decision-making of maize seeding time and the determination of soil sampling distance.