Abstract:The pesticide application plays a leading role in the control of agricultural and forestry pests and diseases, but the improper application of chemical pesticides causes a series of problems. Therefore, the research progress of the key technologies for the pesticide atomizing and nozzles, the inline pesticide mixing, the variable-rate control, the profiling spray, the droplet drift control, the electrostatic spraying, and the intelligent targeting spraying were analyzed. The development of six kinds of typical plant protection machinery was reviewed, that was, anti-droplet-drift sprayers, profiling sprayers, high clearance self-propelled sprayers, weeds control machines, orchard sprayers, and intelligent targeting sprayers. And the development of the aviation plant protection equipment, including the plant protection UAV and its key components was also reviewed. Then, the research concept and general ideas of environment-friendly pesticide spraying machinery, which were the “green and environment-friendly, precision and high efficiency”, were put forward. It was proposed to develop plant protection robots and special-purpose plant protection machinery, such as plant protection robot and its array design, plant protection machinery for specific applications, aviation pesticide application equipment and plant protection UAV. The intelligent IoT-driven pesticide spraying system was introduced, including the intelligent identification of pests targets and the precautionary prevention system, the intelligent plant protection information transmission system through wireless Internet of Things, the three-dimensional intelligent collaborative capability for the pesticide spray system. The key technology of plant protection machinery, including new nozzles and inline pesticide mixing, intelligent and flexible transport platform was put forward. It was suggested that more attention should be paid to help researchers better carry out disruptive innovative research on advanced plant protection machinery.

Abstract:Aiming to solve the problem of that the seed spacing error of corn precision planting caused the seed rate distribution nonuniformity, an online seed spacing monitoring and miss seeding warning system of maize precision planting was designed. The system was mainly composed of an onboard computer, a seeding monitoring electronic control unit (ECU) and related sensors. The corresponding host computer monitoring software and a slave computer program based on an online moving average seed spacing monitoring algorithm were designed for the purpose of achieving seed missing warning according to the seed spacing and its error monitoring on the process of maize precision planting. Firstly, the precise seed dropping monitoring was the premise of seed spacing monitoring, thus a seed count accuracy experiment was designed. The results showed that the 10 simulated travel speeds increased from 3km/h to 12km/h at an interval of 1km/h, the average seed count accuracy of a finger pick-up seed meter and an air suction seed meter was 99.12% and 99.71%, with standard deviation of 0.52% and 0.44%, respectively. The average error of the whole seed count monitoring was less than 1%. Secondly, under the condition of laboratory environment, a system seed spacing monitoring precision experiment based on a highspeed photogrammetry seed spacing testbed was designed. The target seed spacing was set to 25cm, the travel speeds increased from 3km/h to 12km/h at an interval of 1km/h, and the finger pick-up seed meter as the test object. The experimental results showed that the average of absolute seed spacing monitoring error was 2.34cm with standard deviation of 2.56cm. However, there were many seed spacing monitoring outliers. To solve the above problem, a moving average program was designed to analyze the monitoring seed spacings, the results showed that the average absolute of seed spacing monitoring error was 0.79cm with standard deviation of 0.62cm, the maximum of the average seed spacing monitoring error at any travel speed was 1.69cm with standard deviation of 0.23cm. The seed spacing outliers were significantly reduced after moving average processing, and the seed spacing monitoring error was less than 2.00cm. Finally, a test prototype was designed based on an air-suction maize precision planter, and a seed spacing monitoring performance experiment was done in field. In the test, the travel speeds was 5.49km/h and 8.49km/h，respectively, the target seed spacing was set to 25cm, and then 350 continuous emergence spacings were collected. The test results indicated that compared with the emergence moving average spacings, the average seed spacing monitoring error was 1.84cm and 2.22cm, with standard deviation of 1.61cm and 2.13cm, respectively, at the travel speed of 5.49km/h and 8.49km/h. The curve of seed spacing monitoring values were basically coincident with the curve of the emergence moving average spacings values. The online seed spacing monitoring was implemented, and the monitoring precision of the seed spacing uniformity was improved. The results can give a reference to achieve the precise online monitoring system of the maize precision planting uniformity.

Abstract:Aiming at the problems of the unreasonable working width and straw throwing distance of the row cleaners and poor stability of the working width of notillage planter when working in the field, an automatic width control row cleaner was designed. A mathematical model of the row cleaning width was established, the main operating parameters that affected the row cleaning and the missing cleaning area were determined, and the value range of each parameter was obtained. An automatic width control system was designed by using S-type pressure sensor and electric linear actuator. The method of combining orthogonal experiment design and EDEM discrete element simulation technology was adopted, and the optimal working parameter combination of the row cleaners was determined, and the operation performance of the automatic control system and the row cleaners were verified by field experiments. The research results showed that the main working parameters that affected the working width and the missing cleaning area of the row cleaners were the distance of the two cleaners, the working deflection and the working inclination angle. The optimal combination of parameters obtained by the simulation test was that the distance of the two cleaners was 90mm, the working deflection angle was 30° and the working inclination angle was 15°. The automatic control system monitored the pressure of the row cleaners on the soil and adjusted its depth into the soil in real time, thereby regulating the operation width of the row cleaners, which can effectively improve the stability of the width by 8.7 percentage points. Field test results showed that the straw cleaning rate, straw lateral movement distance and width stability of the device were 92.3%, 40.2mm and 92.1%, respectively, which can meet the agronomic and technical requirements of no-tillage planting operations. The research results can provide reference for the design of similar soil contact parts.

Abstract:According to the existing problems of no-till corn seeding in full straw returning in the northeastern part of China, such as the low stubble removal rate in seeding row, the secondary cover of seeding row after sowing caused by the return of straw under the action of wind, a row cleaner with side cutter and stubble clean disk was developed. The stubble-breaking performance was analyzed to determine the type, size and installation parameters of cutter. Through the analysis of the trajectory of straw and topsoil under the action of stubble clean disk, the main parameters of the stubble clean disk affecting the stubble removal rate and the straw returning were clarified as follows: radius of gyration, radius of curvature and length of teeth. The simulation experiment of quadric rotation orthogonal combination was carried out in discrete element software EDEM, taking the stubble removal rate and the amount of straw covering soil as evaluation indexes, the optimal parameter combination of the stubble clean disk was determined as follows: the radius of gyration was 163mm, the radius of curvature was 350mm, and the length of teeth was 52mm. The best parameter combination was used for field verification test, which showed that the stubble removal rate and the amount of straw covering soil were 91.4% and5833cm3/m2 under the 20cm clear width, which was similar to the simulation results. After the operation of the combine of side cutter and stubble clean disk, the straw between the rows was covered by the topsoil of the row and there was no straw return phenomenon, which satisfied the agronomic and technical requirements of no-till seeding.

Abstract:When the 2BMFJ type stubble field no-tillage straw mulching precision seeder is operated in a wide range, the multi-stage lateral movement of corn straw would be retained, which would affect the straw cleaning rate of the planting interval, resulting in the working quality and operating efficiency of the machine and other issues. Through the analysis of the single-stage throwing movement of corn straw, the corn straw lateral movement rectifier was designed, and the mathematical model of the rectifier structure parameters was established. The key structural parameters of operation performance, such as the entry angle, the angle of rectifier package and the radius of rectifier, were proved out, thus the range of key structural parameters was determined. The quadratic regression orthogonal rotation center combination test method was adopted. On the test platform of rectifier for multi-stage lateral movement of corn straws built in the EDEM software, the virtual simulation parameter combination optimization test was carried out with the angle of entry, the angle of rectifier package and the radius of rectifier as the test factors, and the rate of cleaning straw and the consistency of cleaning straw between rows as the performance evaluation indexes. The results showed that each factor had a significant impact on the performance evaluation index. The primary and secondary order of impact on the rate of straw cleaning was from large to small as the angle of rectifier package, the radius of rectifier, and the angle of entry;the primary and secondary order of impact on consistency of straw cleaning from large to small was the angle of entry, the radius of rectifier, and the angle of rectifier package. The interaction between the factors had a significant impact on the rate of straw cleaning, and had no significant impact on the consistency of straw cleaning between the rows;the optimal parameter combination was an entry angle of 61°, a rectification angle of 104°, and a rectifying radius of 424mm. The rate of straw cleaning was 93%, and the consistency of straw cleaning between rows was 93.5%. According to the virtual simulation optimization parameter combination, the rectifier processing and field verification test were carried out. The rate of straw cleaning was 93.5%, and the consistency of straw cleaning between the rows was 93.7%, which was basically consistent with the results of virtual simulation test.

Abstract:Aiming to ensure the efficiency of ridge construction, reduce the labor intensity, and solve the problem that the manual control accuracy was not high and there was a large swing in the process of switching operation mode of the bi-directional ridger in paddy field, resulting in unstable rotation and unable to automatically lock. The whole structure and working principle were described. The motion model of the rotation adjustment device and the locking device was established, the angular acceleration range of the rotation process was determined to be 0.038~0.154rad/s2 and the rated output torque of the motor of the required electric push rod was 1.64~6.71N·m through kinematic and dynamic analysis. Based on the calculation of the moving speed of the electric putter, the relation curve between the elongation speed of the electric putter and the turning time was obtained and the steering control system was designed. According to the stress analysis of the lock pin, the maximum resistance of the lock pin to the soil was 18470N and the diameter of the lock pin was 10mm. The slewing performance test was conducted to record the slewing adjustment state of the chain drive box and compared with the theoretical motion state designed in a rotating cycle. The results showed that the mean square error of the whole rotation cycle was 1.01(°)2, the rotation adjustment process was consistent with the ideal motion process, and the control system had a high precision. Field operation performance test was conducted on the bi-directional ridger in paddy field. The test results showed that the average solidity of the ridge reached 1180kPa, and the rotation adjustment locking device was firmly locked, which did not affect the normal operation in the field.

Abstract:Evolution of agronomic practices has led to the classification of paddy production machines. Specialized machineries also imply differences of functional performance among different types of soil engaging tools. Each class of rotatory tiller knives thus needs to be developed with its specific optimized designs. Puddling is an independent mechanized field operation. Puddling knife has been well evolved from long history of paddy production and industry development. However, proper definition on mechanized puddling and the design theory for puddling knife is lacking. An inverse engineering approach was adopted to quantify the geometric parameters of a market available puddling knife. Puddling operation and the design principle for puddling knife was defined. A rotary tiller knife (modeled R245) was used for comparison purpose and a field experiment was conducted to illustrate functional performances of the puddling knife. Results showed that rank intrusion effect was a key factor guaranteeing the functionality of puddling. Therefore, the key parameter of puddling knife design was defined as side blade rank angle. It was the integrated effect of the side blade rank angle and the side blade width that determined the puddling performance of the knife. Compared with R245, the puddling knife was featured with its side blade rank angle, which static value was higher than the dynamic value. While the R245 had a flattened side blade (i.e. 0° of rank angle). In the front blade section, both the puddling knife and the rotary tiller knife had a similar geometry. Field experiment further proved an improved performance of the puddling knife as compared with R245, either in puddling quality and straw burial. The result also implied the need to apply inverse engineering tools for the design of all other types of soil engaging tools.

Abstract:An interactive layered subsoil shovel was designed on the basis of material (soil, straw and stubble) characteristics, sliding-cut principle and the effect of subsoiler on soil before EDEM simulation analysis in order to improve the stubble breaking ratio of sub-soiling and reduce soil disturbance and energy consumption under the condition of maize straw returning. Firstly, the structure parameters of the front shovel were obtained according to the material characteristic and the sliding-cut principle. Secondly, the shovel handle of the interaction section of the backhoe to form the sliding interaction with the backhoe was designed and the structural parameters of the backhoe were obtained according to the position and direction of the maximum acceleration of the backhoe soil in the simulation process of the front shovel, at the same time, the handle of the upper and lower section of the shovel was designed according to the material characteristics and the sliding-cut principle. Finally, the distance between the front shovel and the backhoe in sliding interaction was in the sliding state according to the moving speed of the front shovel and the time of soil particles returning to the maximum acceleration. The direction of the maximum acceleration of soil particles selected in the simulation process of the interactive layered subsoil shovel and the front shovel was compared to verify the rationality of the design idea. The results of the discrete element simulation showed that it was possible to reduce the soil disturbance effectively through the interactive layered subsoil shovel. In terms of the average force on the stubble, it was increased by 22.14% and 26.98% respectively compared with the ordinary layered subsoil shovel and the circular arc single shovel. In terms of the average resistance, it was declined by 14.25%, 26.02% and 8.71% respectively compared with the non interactive layered subsoil shovel, the ordinary layered subsoil shovel and the circular arc single shovel. The interactive layered subsoil shovel was endowed with the high stubble breaking rate, the small soil disturbance and the low energy consumption, which met the requirements of sub-soiling.

Abstract:Aiming at the low efficiency of rice panicle fertilizer application and the difficulty of mechanization, a rice fertilization spreader system，centrifugal spinning disc and flow regulating device was designed based on multi-rotor UAV for improving the mechanical performance and uniformity of UAV fertilizer application. The structural parameters of rice fertilizer plant were determined, and the effects of disc speed, flow rate, falling position angle, UAV flying speed and flying attitude on fertilizer distribution were analyzed by using EDEM software. The experimental results showed that it was beneficial to determine the width boundary by applying fertilizer in concentric circles. The width was increased with the increase of rotating speed, and the peak value of fertilizer distribution was changed from left to right. With the increase of flow rate, the uniformity presented a trend of first passing and then decreasing. UAV pitch and roll inclination had influence on fertilizer distribution, while pitch caused fertilizer to pile up in the middle, and roll inclination caused fertilizer to pile up in one side. The interaction of flow rate, UAV flight speed and centrifugal disc speed with the falling position angle had a significant effect on the uniformity of the system. When the landing angle was 40°, the disk rotation speed was 1100r/min, the flow rate was 3460 particles per second, and the UAV flight speed was 5m/s, the coefficient of variation was 8.86%. The efficiency of UAV fertilization spreader was about 12.5 times more than that of manual fertilization. This research provided a solution for the mechanized application of rice panicle fertilizer and a reference for the design of the centrifugal UAV rice panicle fertilizer spreader.

Abstract:Aiming to realize the real-time automatic proportioning of various solid fertilizers in the process of variable rate fertilization and improve the accuracy of the fertilization control system, a variable solid-fertilizer closed-loop fertilization control system and corresponding fertilizer apparatus based on the prescription map was designed for nitrogen (N), phosphorus (P) and potassium (K) fertilizers by using increment PID control algorithm. The system mainly included master-slave controllers, prescription map, Beidou positioning module, speed measuring module, human-machinery interface, electric fertilization motors and fertilization monitoring modules. The function of master controller was as follows: receiving the instructions from human-machinery interface, positioning data acquiring from Beidou module, measuring vehicle speed by Hall sensor module, reading theoretical fertilization amount from prescription map, monitoring the working status of electric fertilization motors, and sending the fertilization instructions to slave controller. Humanmachinery interface was used to set or read the operation parameters between master controller and mobile APP. The slave controller received and delivered fertilization motors instructions. According to the fact that small and medium-sized seeders were widely used in field production in China, the laboratory experiments were carried out with the speeds of fertilization applicator setting in the range of 3.5km/h to 6.5km/h, and the maximum and the mean response time of the control system was obtained of 1.85s and 1.45s, respectively. When the fertilization rates were set as 50kg/hm2, 100kg/hm2, 200kg/hm2 and 300kg/hm2 at vehicle speeds of 4km/h, 5km/h, and 6km/h, the accuracy of control and monitoring were more than 97.16% and 98.56%, respectively. In the field experiment in Donghai Village, Shuangcheng District, Harbin, China, with vehicle speeds of 4km/h, 5km/h, 6km/h, the application accuracy of urea, diammonium phosphate and potassium chloride based on a soil prescription map were more than 97.22%, 98.60% and 97.73%, respectively, which met the requirement of precision fertilization system.

Abstract:In view of the lack of suitable machinery for pea harvesting in China, in order to realize the mechanized harvesting of pea crops, the 4SZ-1.2 pea windrower was designed. Firstly, the current situation of China’s pea planting system and pea harvesting technical requirements were thoroughly understood, and the problems such as cutting difficulties, transportation blockage, lay blockage that are easy to occur in the process of mechanical harvesting were comprehensively analyzed. The designed 4SZ-1.2 pea windrower was mainly composed of the cutting device, the anti-winding reeling device and the conveying and laying device, which can effectively realize the cutting, conveying and laying operations of pea crops;then the quadratic regression orthogonal rotation test was used to analyze and determine the optimal parameter combination: the forward speed was 1.03m/s, the cutting speed was 1.19m/s, the conveying speed was 1.22m/s, and the reel rotation speed was 45.97r/min. Finally, the field tests of pea windrower were carried out in Pinggu, Beijing, and the field results showed that the pea placement after the pea windrower was laid neatly, the stubble was cut uniformly. After data sorting, the following data results can be obtained：the missed cut rate was 4.78%, the harvest loss rate was 4.96%, and the operation efficiency was 0.185hm2/h, which met the demand for peas harvesting. The overall structure of the pea windrower was simple and the cost was low. It was hoped that the research result can provide technical support for the application of pea harvesting machinery.

Abstract:In order to solve the problem of lack of special cutting and beating machinery for King grass, the integrated process of King grass cutting and beating was studied, and a small combined King grass cutting and beating machine for family farmers was designed, which met the diversified requirements of King grass processing, and improved the quality of King grass cutting and beating machine working performance. Through theoretical analysis, the structure design and parameter calculation of the key parts such as feeding device, cutter and throwing board were completed. The cutting process of cutter mechanism was simulated by ANSYS/LS-DYNA. According to the orthogonal test, the main and secondary factors affecting cutting power consumption were cutter type, clearance between moving and fixed cutter, fixed cutter height. The notched circular cutter was selected, the clearance between moving and fixed cutter was 2mm, and the height of fixed cutter was 23mm. Through the processing of the prototype and the optimization test of King grass beating, the optimal structure and working parameters of beating process were determined as the spindle speed of 1400r/min, the King grass crushing mode was standard hammer and toothed plate crushing, and the slurry discharge structure was round hole concave plate. On the basis of experimental research, the small King grass chopping and beating machine was improved. The results of the improved performance test showed that the pure working time productivity of King grass cutting was 545kg/h, and the standard grass length rate was 86%;the pure working time productivity of beating was 150kg/h, and the long grass content in pulp was 9.1%.

Abstract:In order to improve the accuracy of the parameters needed in the discrete element simulation process of the corn stalk powder, the Hertz-Mindlin with JKR Bonding contact model in EDEM software was used to calibrate the contact parameters of the discrete element model of corn stalk powder. Firstly, the physical test results of contact parameters were used as the basis for the selection of simulation parameters. Plackett-Burman test was used to screen the initial parameters. The results of variance analysis showed that the rolling friction coefficient between corn stalk powder, static friction coefficient between powder and stainless steel plate and surface energy JKR had significant effects on accumulation angle. Secondly, taking the relative error of accumulation angle as the evaluation index, the quadratic polynomial regression model of accumulation angle and three significant parameters was established by Box-Behnken test. Taking the measured accumulation angle of 42.60° obtained by physical experiment as the target value, the significance parameters were optimized, and the optimal combination was obtained as follows: corn stalk powder-corn stalk powder rolling friction coefficient was 0.05, corn stalk powder-stainless steel plate static friction coefficient was 0.47, and surface energy was 0.05J/m2.Finally, the accumulation angle and die hole compression test were compared under the calibrated parameters. The results showed that the relative error of simulation accumulation angle and measured accumulation angle was 0.68%, and the relative error of simulation compression displacement and measured compression displacement was 0.98%. By comparing and analyzing the fitting of the change curve of the compression displacement of the corn stalk powder in the mold hole in the two tests, the coefficient of determination between the two curves was 0.9627. The parameters obtained from the calibration can be used for DEM simulation of corn stalk powder mold hole densification.

Abstract:Aiming at the problems of high intensity and low efficiency of manual peeling of sweet potato, a technological scheme for adaptive cutter mechanism peeling of sweet potato was proposed based on the analysis of the shape characteristic of sweet potato. To ensure the consistency of sweet potato cutting thickness and the stability of peeling operation，firstly, a theoretical analysis of the sweet potato peeling process was carried out, and the structural parameters and value ranges of key components that affected the peeling performance were determined. For further study of the improved working performance of mechanism peeling for sweet potato, the Box-Behnken experimental design with three factors and three levels was performed, parameters such as radius of profiling ball, profiling force and rotational speed ratio of electric cylinder and jaw were selected as independent variables, which would influence the thickness uniformity of sweet potato peeling. The mathematical model was established, and the main reasons that affected peeling performance were analyzed as well. The test results showed that effects orders of three parameters on the sweet potato obvious rate from primary to secondary were as follows: profiling force, roller ball size, profiling force and rotational speed ratio of electric cylinder and jaw. The optimal working parameters were forward profiling force of 68.1N and radius of profiling ball of 28.1mm and rotational speed ratio of electric cylinder and jaw of 0.95. Confirmatory tests showed that the sweet potato peeling machine had a better peeling effect，the average peeling time of single sweet potato was 10s, the working efficiency was 360 pieces/h, which can meet the design requirements of the sweet potato mechanical peeling with the optimized working parameters.

Abstract:In order to solve the poor accuracy and low stability problems of the real-time measurement system of paddy rice moisture content in combine harvester, a real-time sampling platform for paddy rice was set up, based on which the real-time measurement system of paddy rice moisture content in combine harvester was designed by capacitance method. The hardware circuit of the detection system was designed and the monitor interface of the master computer was developed to realize the communication between the master computer and the slave computer. The master computer can display and store monitoring information in real time. The influence of temperature and impurity rate on capacitance difference was analyzed and the performance of the sampling platform was verified. The relation models of capacitance difference, temperature and paddy rice moisture content output were established. The coefficient of determination was 0.9866, and the model was verified. The results showed that the impurity ratio of paddy rice was less than 1.2% after sieving by sampling platform. The maximum error of the detection system in laboratory testing was 0.42%, the average error was 0.22%, the average relative error of the detection device was no more than 1.25%. In the field validation test, the relative error of the detection device was less than 3%, which effectively improved the accuracy of on-line detection. The research result can provide a reference for on-line access of paddy rice moisture content.

Abstract:In view of the problems of low efficiency of artificial separation, high rate of wrong separation and lack of automatic detection and separation device of mildewed corn seeds in China, a real-time detection and separation device was designed. The device consisted of a feeding unit, a detection unit, a sorting unit and a control system. The MSP430 was used to communicate by lower computer with the upper computer in real time and control the sorting actuator. The Matlab 2014b software was used by upper computer to process the corn seed image in real time and output the recognition results. In order to facilitate the image acquisition of corn seeds, a seed separation mechanism was designed. Based on the difference of surface color between mildewed corn seeds and normal corn seeds, an image algorithm for identifying mildewed corn seeds simultaneously in a single image was designed based on HSV spatial color division of image. The algorithm can realize pixel level mildewed corn seed recognition, and a sort strategy of corn seeds was proposed to achieve accurate sorting. The experiment showed that the device can realize the real-time detection and sorting of mildewed corn seeds. The acquisition and processing time of a single image was about 0.7s, the highest sorting speed was 680 grains per minute, the recognition accuracy of mildewed corn seeds was 100%, and the overall sorting accuracy was no less than 94%. The device realized the whole process of corn from feeding to sorting automatically, and can achieve the purpose of real-time detection and sorting of moldy corn seeds. The device adopted machine vision technology to carry out non-destructive detection of corn seeds, which provided technical support for the related research and development of later corn seed grading system.

Abstract:Timely and accurate crop monitoring and grain yield prediction before harvest of winter wheat are helpful for accurate farmland management and decision-making. Aiming to explore the potential of multitemporal vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) based multispectral images in the whole growth period of winter wheat and improve the grain yield prediction, a UAV platform carrying multispectral camera was employed to collect the high resolution images of the whole growth period of winter wheat under different water deficit states. Different kinds of multispectral VIs were used to characterize the growth characteristics of winter wheat and the correlations between VIs and winter wheat grain yield were analyzed. The multi-temporal VIs were collected to form the data set, which was used to train the machine learning algorithm. Three algorithms, including partial least squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR) were used to predict the grain yield of winter wheat. The results showed that with the growth of winter wheat, the leaf area index (LAI) was changed basically as parabolic, indicating the useful of MTVI2 in remote sensing retrieval of LAI. Meanwhile, the correlation coefficient between multiple VIs and grain yield was continually increased to 0.7 at the end of the filling stage. The linear regression determination coefficient (R2) between VIs and grain yield also reached the maximum. Moreover, the accuracy of VIs forecasting grain yield was also continuously improved, because of the multi-temporal VIs reflecting the changing characteristics of winter wheat growth. The multi-temporal VIs at the flowering and early stage of filling had higher accuracy than the VIs at a single growth period. For instance, the R2 of PLSR was increased by about 0.021 and the R2 of SVR was increased by about 0.015 and the R2 of RFR was increased by about 0.051. For the multitemporal vegetation index at the end of filling stage, different models had high estimation accuracy. The highest R2 and RMSE of PLSR were 0.459 and 1822.746kg/hm2, the highest R2 and RMSE of SVR were 0.540 and 1676.520kg/hm2and the highest R2 and RMSE of RFR were 0.560 and 1633.896kg/hm2, respectively. So the RFR trained in this data set had the highest estimation accuracy and better stability. These findings demonstrated that the proposed approach can improve the prediction accuracy of grain yield as well as achieve an efficient monitoring of crop growth. Under water deficit conditions, longterm water deficit had a great impact on the growth of winter wheat at the filling stage, in turn leading to a decline of winter wheat grain yield. In comparison with normal quantity of irrigation water, the long-term water deficit caused a decrease in winter wheat production by about 1/2.

Abstract:To validate the feasibility of particle filter assimilation algorithm for crop yield estimation, and improve accuracy of summer maize yield estimation in the central plain of Hebei Province, the leaf area index (LAI) and vegetation temperature condition index(VTCI) simulated by the CERES-Maize model were assimilated with the state variables retrieved from MODIS data. The random forest method was used for determining the weights of different variables at the growth stages of summer maize. The maize yield estimation model was established based on the weights of variables and measured yield. The results showed that no matter at the sampling sites or at regional scale, the assimilated LAI and VTCI were better able to respond the monitored LAI and VTCI, the assimilated LAI decreased the sharp changing points which LAIs were simulated by CERES-Maize, the assimilated VTCI was in good agreement with those of both the remotely sensed VTCI and the simulated VTCI, and the assimilated VTCI was a good index for indicating crop water stress of summer maize. The optimal model was selected for summer maize yield and estimation accuracy of the year 2015 in the central plain of Hebei Province, normalized root mean square error (NRMSE) between simulated and observed summer maize yields before and after performing PF assimilation scheme was decreased from 12.71% to 10.50%, and relative error (RE) was decreased from 12.57% to 8.43%. Therefore, the established yield model based on the assimilated LAI and VTCI fully integrated the advantages of remote sensing information and crop model, and can be used for estimating summer maize yield accurately.

Abstract:Sugar beet is the world’s main sugar production crop and one of the recognized alternative materials for biofuel production. Plant height of sugar beet can be used to estimate root biomass, indicate water stress, and can also be an effective indicator of nitrogen content and yield. It is an important parameter for breeders and farm managers to assess the growth status of sugar beet in the field. The rotary-wing UAV platform has the characteristics of vertical lifting, fixed-point hovering, and strong maneuverability. It is suitable for obtaining multi-scale, multi-repeat, fixed-point, and high-resolution farmland crop information. Totally 186 genotypes of sugar beet were chosen to explore accuracy difference of estimated plant height for UAV-RGB and UAV-LiDAR system, and to do comparison with the measured value. The correlation between estimated plant height by LiDAR and measured value (straight slope was 0.99, R2 was 0.88, rRMSE was 6.6%) was higher than that measured by RGB (straight slope was 0.94, R2 was 0.8, rRMSE was 9%). Further stratification analysis of point clouds was carried out to compare the difference of point clouds distribution in the canopy. For the later growth stage with relative dense canopy, UAV-LiDAR can reconstruct a more complete three-dimensional canopy structure than that of UAV-RGB system. 

Abstract:In order to accurately obtain the field crop planting distribution information to satisfy the needs of refining the management of agriculture, a field crop classification method was proposed for unmanned aerial vehicle (UAV) multispectral remote sensing images based on DeepLab V3+ network. In which the structure of the input layer was modified to fuse multispectral information with the prior features of vegetation indexes, and the activation function of Swish was adopted to maintain the backpropagation capability of the model when the response was a negative value. The research region was Shahaoqu irrigation field in the Hetao Irrigation District, Inner Mongolia Autonomous Region, whose UAV multispectral remote sensing images collected in 2018 and 2019 were taken as samples. The classification model was constructed and trained on the data of 2018, and the generalization performance of the model was tested on the data of 2019. The experimental results showed that the improved DeepLab V3+ model got excellent classification with fast speed. Its mean pixel accuracy and mean intersection over union were 93.06% and 87.12%, respectively, which were 17.75 percentage points and 20.8 percentage points higher than those of the traditional support vector machine (SVM) method using artificial features, and 2.56 percentage points and 2.85 percentage points higher than those of the original DeepLab V3+ model. Therefore, this method can learn more expressive semantic features from the field crop remote sensing images, thus obtaining accurate crop classification. The research result provided a new technical basis for the interpretation of farmland types using UAV remote sensing images.

Abstract:In order to study the ability of spectrum to estimate the water content of winter wheat plant components and analyze the separation rule of spectral information by wavelet technology, the canopy spectral information of winter wheat and the corresponding measured values of water content of leaves, stems and ears of winter wheat were used as data sources. Then, the partial least squares (PLS) algorithm was used to construct the estimation model of winter wheat plant component water content, which was verified and evaluated. The results showed that after wavelet technology decomposition, the absorption characteristics of winter wheat canopy spectrum were separated into high frequency information by decomposition level, and the absorption characteristics represented by each decomposition level were distributed in the H1~H10 decomposition level of high-frequency information. The accuracy and stability of the estimation model of winter wheat ear water content was strong, that of the stem was the second, and the leaf stability was the worst. This showed that the current situation of water supply of winter wheat after poplar flowering stage was no longer suitable to use only leaf water content for evaluation, and the detection index should be added or replaced.

Abstract:In order to realize the rapid and accurate detection of chlorophyll content in plants, an inversion method based on MMD migration was proposed. Taking Epipremnum aureum leaves as the research object, the motion trajectory of photons was simulated with the Monte Carlo method based single-layer flat plate model, totally 12000 simulated luminance distribution maps were obtained. The convolutional neural network was used to train the simulated spectral data to obtain the pre-training model. Then based on the pretraining model, the model was fine-tuning on the measured spectral data of a small amount of Epipremnum aureum leaves to realize the inversion of the optical parameters. The inversion results were as follows: absorption coefficient μa was 84.83% and scattering coefficient μs was 83.33%. On this basis, the maximum mean difference method was added to improve the migration effect. The results showed that the MMD migration method had a better inversion effect with absorption coefficient μa was 87.55% and scattering coefficient μs was 86.67% compared with the common model migration method. The chlorophyll regression model was established by using the full connection layer characteristics obtained from MMD migration, and the determination coefficient R2 of this method was 0.0468 and 0.0620 higher than that of the model established directly using optical parameters and spectral images, respectively. The experimental results showed that the inversion method based on optical characteristic parameters can provide important reference for the research of chlorophyll nondestructive detection.

Abstract:Aiming to solve the problem of difficulty in disease recognition and low application rate of recognition model in complex field environment, a corn disease recognition method based on improved CNN was proposed. The influence of data set size and quality on the performance of disease recognition model was discussed. The complicated background images were used and preprocessed by using augmentation, background removal, local fine segmentation and normalized processing methods. Then the CNN structure was designed by using five convolutional layers, four pooling layers and two fully connected layers. The L2 regularization and Dropout strategy were utilized to optimize the network. The results showed that the optimized model achieved an average precision of 97.10% implemented in nine kinds of diseases images with complicated background, which was increased by 9.02 percentage points than that of unimproved CNN method. Compared with the model with original sample, the average precision of the model trained with augmented data was increased by 28.17 percentage points. The removal of complex background can eliminate the influence of environmental noise on the model, thus the performance can be further enhanced, which reached an average precision of 97.96%. After the local fine segmentation of data set, the average precision was raised up to 99.12%. These indicated that CNN needed a large number of representative and high quality training data to identify the target feature. On the basis of improved CNN, a corn field disease recognition system based on mobile was developed, which achieved 83.33% recognition accuracy when verified by experiment in the field. The research could provide guidance for disease recognition and precise prevention and control in corn field.

Abstract:Taking 80 black soil samples collected from Nehe City, Heilongjiang Province, and hyperspectral measured data as data sources, the spectral reflectance and its feature changes of copper (Cu), zinc (Zn), manganese (Mn) in black soil were analyzed, the correlations between four different forms of spectral reflectance, which included original, first-order differential, continuum removal, and firstorder derivative of continuum removal and soil Cu, Zn, Mn contents were calculated, and the correlation coefficient method was used to extract sensitive bands. Then the kernel principal component analysis (KPCA) was applied for dimension reduction and feature extraction of hyperspectral sensitive band data, and the feature information was input into extreme learning machine (ELM), and the KPCA-ELM estimation model was constructed to quantitatively estimate the heavy metal contents. The results showed that KPCA had a strong ability to extract nonlinear features and effectively selected the optimal variable set. The KPCA-ELM model was feasible in predicting soil element content and the determination coefficients of the three heavy metal elements were all more than 0.6, where the prediction accuracy of Zn was the highest among the three heavy metal elements. And the determination coefficient and root mean square error were 0.805 and 3.275mg/kg respectively, which were improved by 14.0% and 18.5% compared with without feature extraction. Therefore, KPCA-ELM model was a fast and feasible method for hyperspectral estimation of heavy metal content.

Abstract:The effectiveness of airborne LiDAR point cloud and aerial imagery on tree species classification and the effect of XGBoost algorithm for feature selection on tree species classification accuracy were researched, and the ability three non-parametric classifiers of random forest, support vector machine and artificial neural network to classify tree species on a single-wood scale were evaluated. Aiming at the current background effect of canopy extraction and the problem of over-segmentation, the traditional single tree canopy segmentation method was improved by using the visible light difference vegetation index and bilateral filtering;and then the single tree canopy was used as an object to extract multi-dimensional features by using the XGBoost algorithm to perform feature importance ranking and feature selection. Finally, three non-parameter classifiers of random forest, support vector machine and artificial neural network were used to design 12 classification schemes to classify single tree species and do accuracy evaluation. The results showed that the improved single tree segmentation method can effectively improve the accuracy of tree crown extraction, and the accuracy of the obtained tree canopy segmentation results was more than 80%;the LiDAR data and aerial orthophotos were combined, and the ANN classifier was used for feature selection after XGBoost algorithm for feature selection. The scheme had the highest accuracy, with an overall accuracy of 86.19%, indicating that multi-source data synergy and feature selection can improve the accuracy of tree species classification. The ANN classifier had the strongest ability to classify existing tree species on a single tree scale.

Abstract:The application of electric unmanned aerial vehicles (UAVs) in agricultural remote sensing monitoring is limited by their short endurance. A long-endurance battery-powered light blended-wing-body UAV was designed for agricultural remote sensing. A design method for a light blended-wing-body UAV was proposed. The structural parameters of the light fixed-wing UAV was optimized and a model was built. The aero-dynamic performance of the UAV was determined by computational fluid dynamics (CFD), and the force distribution in flight state was dynamically analyzed based on fluid-structure interaction model. The results showed that the lift-to-drag ratio of the optimized model was 2.6% higher than that of the initially designed model, and the light blended-wing-body UAV had good aerodynamic characteristics. The pressure distribution of the UAV was effective at a 6° angle of attack and a cruising speed of 15.5m/s. When the take-off weight was 1.5kg, the maximum value of pressure on the lower surface of the UAV was 143Pa, and the lift force was mainly concentrated on the leading edge of the wing. The theoretical endurance time was 65min, and the maximum stress during the cruising phase was lower than the tensile strength of the fuselage material and the yield strength of the rotor. Under the same conditions, the maximum value of total deformation was 0.28838mm. The above parameters all met the normal working conditions of the UAV. Therefore, the structure, materials, and performance of the proposed light agricultural remote sensing UAV were suitable for agricultural remote sensing. It also provided a design method and key technologies for the application of electric drones.

Abstract:The maximum temperature of the dairy cow’s eye area is highly correlated with the widely used rectal temperature. The existing methods have not been able to automatically extract the eye temperature from the thermal infrared image. In order to achieve non-contact, automatic and high-precision detection of dairy cow’s eye temperature, a method for automatic detection of dairy cow’s eye temperature based on thermal infrared imaging technology and skeleton tree model was studied and proposed. On the basis of the thermal infrared image of the side of dairy cow, the threshold segmentation method based on the gap measurement was used to extract the dairy cow target, and the precise extraction of the dairy cow skeleton was realized and the dairy cow skeleton tree model was constructed. In this model, the head area of dairy cattle was accurately located, and then according to the shape characteristics of the head outline and the geometric position characteristics of the eyes, the center point of the eye area of dairy cattle was accurately located. Finally, the eye temperature was automatically detected in the thermal infrared image of dairy cow with the center point of the eye as the center and the highest temperature in the 20 pixels radius area as the eye temperature. In order to verify the effectiveness of this method, totally 100 thermal infrared images from 50 dairy cows were randomly selected for the test. The results showed that the average absolute error of eye temperature was 0.35℃, and the average relative error was 0.38%. The method had high accuracy and can provide technical support for the non-contact, automatic and high-precision detection of dairy cow temperature.

Abstract:It is of vital significance to detect pig’s eating and drinking behavior by using intelligent method, and analyze the law of eating and drinking water, which plays an important role in early warning of pig disease and maintaining pig welfare. Pig diet behavior detection model based on YOLOv4 was proposed. Aiming at the pig diet image with multi time period, multi view angle and different degrees of occlusion, the database of pig eating behavior image was established. The in-depth feature extraction and high-precision detection classification characteristics of YOLOv4 deep learning network were used to accurately detect pig eating behavior. The results from the whole experiments showed that the model based on YOLOv4 can accurately predict the diet behavior of pigs in different angles of view, different degrees of occlusion and different illuminations. The average detection accuracy (mAP) was 95.5%, which was 2.8 percentage points and 3.6 percentage points higher than that of the same series of YOLOv3 and Tiny-YOLOv4 models, 1.5 percentage points higher than that of Faster R-CNN model, 5.9 percentage points higher than that of RetinaNet model and 5 percentage points higher than that of SSD model. This method can accurately predict the occurrence of pig eating behavior and provide targeted and adaptive technical support for pig intelligent breeding and management.

Abstract:The data of weeds, pests, diseases and cultivation management of rice extracted from agricultural text data is a typical text classification problem, which is fundamental to key text information extraction, text data mining and agricultural intelligent question and answer. The classification of Chinese texts, especially agricultural texts, is characterized by poor data redundancy, sparsity and normativity. While the deep learning technology can automatically extract the key features of the text, and the built model has strong adaptability and mobility. For that reason, in order to solve the problem of classification performance of the model deteriorates caused by inaccurate text feature extraction and deepened network hierarchy, a text classification method of rice knowledge oriented Q&A system was proposed. The Python of scrapy was adopted to obtain Chinese text data on rice pests, grass pests, cultivation and management, such as the experts online system of Hownet and the planting question and answer website, as training and test samples. Jieba segmentation method was applied to rice knowledge text for word segmentation to remove useless symbols and stop words in the text. Meanwhile, the results of Chinese segmentation were greatly influenced by the segmentation lexicon. In order to improve the precision of word segmentation of rice knowledge text and reduce the situation of misclassification, omission and misclassification, a ricerelated corpus was constructed on the basis of sogou agricultural corpus, which further expanded the basic Jieba word segmentation database and improved the identification degree of specialized words such as rice diseases, insect pests, grass and drugs, cultivation and management. At the same time, Word2Vec method was used to vectorize text data, and it was compared with One-Hot, TF-IDF and Hashing methods, and it was concluded that Word2Vec method can effectively solve the text vector typical problems such as sparsity and incomplete information. Based on the fundamental structure of ResNet, nine kinds of rice knowledge text classification models were constructed by means of the change and design of its residual module and network hierarchy. The test results indicated that a network with 4-layer residual module structure had good feature extraction accuracy, and the Top-1 accuracy was 99.79%. In the convolutional neural network, the pooling layer was used for the under-sampling operation, which would lose certain text phrase relative position characteristics in the pooling process, thus affecting the classification accuracy of the model, therefore, the optimized 4-layer residual module structure was taken as the basic structure, and the CapsNet was used to replace the pooling layer, and a rice knowledge text classification model, referred to as RIC-Net, was designed. Through comparative analysis of six text classification models, including FastText, BiLSTM, Atten-BiGRU, RCNN, DPCNN and TextCNN, it was concluded that the text classification model designed was able to precisely classify rice knowledge texts with different sample sizes and different levels of complexity, which enabled the accuracy rate, recall rate and F1 value of the model to be no less than 95.17%, 95.83% and 95.50%, respectively, and the accuracy rate was as high as 98.62%. The model can realize accurate and efficient classification of rice knowledge text, meeting practical application requirements.

Abstract:Taking 132 counties and districts in Shandong Province as the research units, the multi-function evaluation index system of cultivated land was established to evaluate the spatial-temporal differences of multi-function of cultivated land, and the spatial-temporal characteristics of the coupling effect of cultivated land function were analyzed by using coupling coordination degree model and the spatial autocorrelation model. The results showed that from 2005 to 2015, the spatial-temporal pattern changes of each single function of cultivated land in Shandong Province were obviously different, and the comprehensive function continued to grow, mainly at the median level. Agricultural production function and social security function had a strong role in promoting the growth of the comprehensive function of the cultivated land, while landscape pattern function and ecological environment function had relatively little impact on comprehensive function of the cultivated land. During the study period, the degree of multi-function coupling coordination of cultivated land was gradually increased from the antagonistic stage to the running-in stage with obvious regional differences. The level of coupling coordination degree in key areas of agricultural development was higher than that in key areas of economic development. The multi-function coupling coordination degree of cultivated land had high spatial aggregation, but the spatial correlation was gradually decreased. The significant H-H area and significant L-L area had strong aggregation, and the number of significant L-H area and significant H-L area was small and scattered with no obvious rule. The research results can provide basis for the regional differentiated management of cultivated land, the coordinated development of multi-functions of cultivated land, and the enhancement of the comprehensive functions of cultivated land.

Abstract:The urban ecological space network is the key link to the natural environment and ecosystem, which is also the foundation for maintaining urban ecological security. In the current urbanization process, the core ecological planning based on the central urban area ignores the dynamic changes of the space between nature and the city, which leads to the incoordination of the ecological environment of urban and rural human settlements, and the method and theory of ecological network simulation and analysis are still in development stage. Taking the construction of the ecological space of Tangshan City as the starting point, based on the morphological spatial pattern analysis method (MSPA) and the minimum cumulative resistance model (MCR) to identify the city’s ecological sources, and the potential ecological corridors between the ecological sources were extracted, combined with gravity the importance of corridors was quantitatively analyzed and the ecological space network of Tangshan City was constructed. The results showed that the extraction and identification of ecological sources was an important node in the construction of urban ecological network. Through identification and analysis, there were 33642 urban ecological sources, covering an area of 245674.52hm2, and mainly distributed in the northern mountain protection area, the central forest ecological protection area and the southern wetland protection area, the landscape types were mostly grassland and woodland, followed by water bodies. By quantifying the importance of landscape elements to the ecological space through patch importance index and other quantified importance, totally 30 important ecological sources were identified as key nodes of ecological corridors. Based on the minimum cumulative resistance and gravity model, totally 435 potential key ecological corridors were calculated and constructed, accounting for 51.21%, 30.74% and 16.46% of grassland landscape, woodland landscape and water landscape, respectively. In addition, the ecological forces and the suitability of the corridors between the source areas were significantly different. The interaction force between the sources 14 and 17 was 14562.77, and the interaction force between the sources 2 and 21 was the smallest of 1.06. Among them, source area 25 (Douhe River Basin in Kaiping District) and source area 18 (Qinglongshan Provincial Nature Reserve of Luanzhou City) were the key ecological source areas for the ecological exchange between the north and the south. In the future work, the protection and management should be focused on. The research result can provide effective support for land space planning, ecological space construction and ecological protection red line control in Tangshan City, and also can provide reference and demonstration for similar regional ecological protection management and construction of ecological space network.

Abstract:Accurate and rapid estimation of evapotranspiration in farmland is significant for precise irrigation management and optimal allocation of water resources. Based on 24 cluster lysimeters, irrigation experiments of winter wheat and summer maize were carried out in 2019—2020, in Daxing Water-saving Irrigation Experimental Station of IWHR, Beijing. Daily data of crop canopy temperatures (Tc) and air temperatures (Ta), and daily evapotranspiration measured by lysimeter (ETa) were observed, to calibrate and validate the named S-I model, which was simple and valid to estimate field crops ET. Then the changes and values of feature parameters of S-I model, a and b, were analyzed and concluded for winter wheat and summer maize, respectively. Results showed that for winter wheat, the characteristic parameter a of the S-I model was increased firstly and decreased subsequently with time change in day. In the case of severe water stress, a was negative and the value was small, and in the case of other irrigation treatments, a was positive and gradually changed to negative. The characteristic parameter b in different irrigation treatment was negative. The parameter b of adequate irrigation treatment was gradually increased with time changes in the day, while the value of severe water stress treatment was relatively large, and the trend in day was unstable. For summer maize, the effect of water stress on model parameters was lower than that of winter wheat. The characteristic parameter a was positive, while the parameter b was negative, which was gradually increased with time. The variation range of parameter b under water stress treatment was significantly smaller than that under other two treatments. The characteristic parameters of drought treatment was changed greatly within days. Model parameters a and b varied greatly between winter wheat and summer maize under different treatments, but the fitting accuracy of (canopy temperature Tc-air temperature Ta) and (daily evapotranspiration ETd-daily net radiation Rnd ) was the highest at 13:00. At this time, model parameters a and b of full irrigation were 1.082 and -1.127 for winter wheat, 1.588 and -1.363 for summer maize, respectively. The determination coefficient (R2) and consistency coefficient d were above 0.7 and 0.9 between the model ETd and measured ETa during the growth period of winter wheat and summer maize, while the values of root mean square error (RMSE) were less than 0.89mm/d. As a contrast, the values of R2 and d in the full irrigation treatment were both higher, and the RMSE was lower than that in other treatments, indicating that water stress affected the estimation accuracy of the model, and the S-I model could be better to estimate the crop evapotranspiration under less water stress.

Abstract:It is a useful and powerful tool to estimate crop evapotranspiration (ET) based on remote sensing technology for evaluation of water use efficiency and precision agriculture. To improve the accuracy of estimation, field continuous monitoring system and remote sensing data of Sentinel-2 were employed here to figure up the maize evapotranspiration. The hybrid dual-source scheme and trapezoid framework-based evapotranspiration model (HTEM), was selected as ET estimating model. The experimental fields located in Zhongwei City, Ningxia Hui Autonomous Region, and the main observations of the study area were during maize growing season (from May to Aug.) in 2019. Sentinel-2 data and observed canopy temperature data were the key input data of the HTEM model. Water balance method was also used for comparisons and evaluation of remote sensed ET. Results showed that Sentinel-2 data had high spatial and temporal resolution, which can match the complex planting plots in the study area, reducing the presence of mixed pixels. Remote sensing inversion parameters fit well with ground observation data. The root mean square error (RMSE) of net radiation over study area during study period was 36.256W/m2. Daily evapotranspiration of two maize experimental fields based on HTEM model were 4.269mm/d and 4.339mm/d, respectively. And total ET of study area were 525.114mm and 533.690mm during main growing period, respectively. The absolute error between remote sensed ET and calculated ET based on water balance method were 13.533mm and 7.774mm, respectively. Evaporation and transpiration can be also obtained based on HTEM model. According to the results, total transpiration of maize during study period were 363.483mm and 358.196mm, respectively. Water was mainly consumed by soil evaporation during the early growth phase. In the middle and late growth phase, transpiration became the main water consumption way. Thus it can be seen that Sentinel-2 data associated with site canopy temperature system would be more available in regional maize evapotranspiration estimation with high precision. These results could also provide technical support for regional crop evapotranspiration research and agricultural water management.

Abstract:In order to ascertain the sustained influence of the application of biochar on sloping farmland in black soil region of Northeast China, field runoff plots with 1.5°, 3° and 5° of typical slops in the region was chosen, where the 4-years experiments were conducted, and soil structure and soil nutrients were observed. In 2016, before the start of the experiment, biochar derived from corn straw was applied with 75t/hm2 in the soil, and a control group was without biochar application on each slope plots, counted up to six plots. Biochar was not applied in subsequent years. The results showed that the application of biochar can increase soil gas phase and liquid phase ratio, improve soil aeration, improve soil threephase ratio, and then increase porosity by 2.83%~5.56% and decrease bulk density by 1.89%~3.62%. The contents of organic matter, ammonium nitrogen and available potassium in the soil were significantly increased after the application of biochar, increased by 9.54%~18.21%, 21.35%~28.02%, and 11.99%~22.71%, respectively. And the effects of above was weakened with time. A random forest regression model was used to evaluate the comprehensive fertility grading indexes and fit the regression equation to predict the indexes of 2020—2022. Comparing fertility changes, it was concluded that the reasonable and effective life for one time application of biochar on soil fertility in the region would be 6~7 years.

Abstract:To find out the optimized water and fertilizer management, field experiments were conducted to investigate the coupling effects of different amounts of water and fertilizers on water consumption, dry matter accumulation on the ground, wheat yield, water use efficiency, nitrogen partial fertilizer productivity as well as economic benefits. Experiments included three levels of irrigation, i.e. 60%ETc (I1, 300.0mm), 75%ETc(I2, 370.0mm) and ETc (I3, 495.0mm), and three levels of nitrogen, i.e. 180.0kg/hm2 (N1), 255.0kg/hm2 (N2) and 330.0kg/hm2 (N3). The irrigation had a significant effect on the water consumption, and water consumption was increased with the increase of irrigation quota. Only appropriate water and fertilizer ratio can obtain higher dry matter accumulation on the ground, yield and economic benefits, and excessive water and fertilizer input would not increase sustainable output and income. Yield, water use efficiency, nitrogen partial fertilizer productivity and economic benefits had significant correlation with water and fertilizer. The quadratic regression equations of each index and water and fertilizer were established and the corresponding water and fertilizer amount of the maximum value of each index was 410.0mm, 260.0kg/hm2, 370.0mm, 260.0kg/hm2, 410.0mm, 180.0kg/hm2, 400.0mm, 250.0kg/hm2, respectively. By regression and spatial analysis, it was concluded that the yield, water use efficiency and economic benefits reached no less than 95% of their maximum values simultaneously when the irrigation quantity was 359.8~428.9mm (72.6%ETc~87.5%ETc) and the nitrogen application was 225.4~280.9kg/hm2, which can provide a scientific basis for irrigation and fertilization optimization in study area and other regions with similar environments.

Abstract:Bean curd wastewater (BCW) is the main waste in the processing of bean products. It is expected to save water resources and reutilize the organic matter in BCW by using BCW instead of pure water as reaction solvent of hydrothermal carbonization (HTC). However, the mechanism of the effect of BCW on the HTC remained unknown. To investigate the effect of different BCW dosages on the HTC of biomass, the HTC experiments of reed stalk under BCW and pure water were carried out at 250℃ for 240min at liquidsolid ratio of 5~30. The elemental composition and fuel characteristics of hydrochar were analyzed in depth. The results showed that with the increase of liquid-solid ratio, the yield of hydrochar obtained under the BCW was increased from 49.27% to 63.53% while that obtained under the pure water was decreased from 46.30% to 38.30%. The C and N content of hydrochar obtained under the BCW was 68.35%~70.30% and 1.48%~3.26% respectively, which was higher than the corresponding parameters of hydrochar obtained under pure water. Moreover, the C and N recovery efficiency of hydrochar obtained under the BCW was increased as the liquid-solid ratio was enhanced. The BCW promoted the decarboxylation reaction during hydrothermal carbonization of reed stalk. Both H/C and O/C atomic ratios of hydrochar obtained under the BCW were close to those of lignite. The higher heating value (HHV) of hydrochar was up to 29.53MJ/kg. The energy densification was about 1.80. And the energetic recovery efficiency of hydrochar was increased from 0.85 to 1.17, while the fuel ratio of hydrochar was decreased from 1.09 to 0.78. When the liquid-solid ratio was less than 10, the combustion characteristics of hydrochar under the BCW were similar to those of hydrochar obtained under pure water, indicating good combustion performance for hydrochar. Thus, the liquid-solid ratio of BCW and biomass should be less than 10 when hydrochar was used as fuel.

Abstract:Camellia oil, as a healthy and high-quality vegetable oil, has great economic value while being popular in the market. Low-field nuclear magnetic resonance (LF-NMR) technology is widely used in the field of food detection due to its rapid and non-destructive characteristics, but the current application of LF-NMR technology in the detection of counterfeit and adulteration of camellia oil remains to be studied. Low-field two-dimensional nuclear magnetic resonance （LF-2D-NMR）can provide abundant relaxation information. Based on the study of the T1-T2 LF-2D-NMR spectrum information of eight edible oils in the fresh and oxidized state, the identification models of camellia oil with other edible oils, oxidized edible oils and the quantitative models of camellia oil blended with corn oil, soybean oil and sunflower oil was established combined with chemometric methods. The results showed that the LF-2D-NMR information of different kinds of edible oils was obviously different;the LF-2D-NMR information between oxidized edible oil and normal edible oil was significantly different;LF-2D-NMR can provide richer characteristic information. The PLS-DA model of camellia oil with other seven kinds of edible oils or eight kinds of oxidized edible oils had excellent classification effect, the accuracy of discrimination both were 100%. In addition, PLS model of camellia oil blended with corn oil, soybean oil, and linseed oil were also established. Studies showed that low-field T1-T2 two-dimensional nuclear magnetic resonance technology can play an important role in the identification of edible oil types and camellia oil adulteration.

Abstract:The stability of soybean protein emulsion depends on the stability of protein membrane at oil-water interface. It is important to study the structure and functional property of interface protein of different protein emulsions. Totally 12 varieties of different interface protein were extracted. The interface flexible protein had higher content of hydrophobic amino acids, and the molecular weight of the protein contained mainly more than 100ku. The globulin subunits of 7S and 11S protein were analyzed by FTIR. The content of α-helix was lower and the content of random coil was higher, which was consistent with the above results of different flexible SPIs. From the composition of SDS-PAGE subunits, it could be seen that the proportion of 7S in the flexible protein was increased, which further proved that 7S protein had higher flexibility. The flexibility of the interface protein was higher. The result of surface hydrophobicity of interface protein was the same as that of SPI, and the interface protein had more hydrophobic residues to be stable at the water-oil interface. From the analysis of the tertiary structure of the interface protein by UV and fluorescence spectrum, it could be seen that the amino acid residues of the interface flexible protein were more exposed and the structure was more flexible, which was conducive to the molecular rearrangement at the interface. The functional properties were also changed, the solubility was decreased and the emulsification was increased, which was related to the structure features of the interface proteins.

Abstract:In order to effectively promote the dissolution of okara insoluble dietary fiber and its functional characteristics, okara (soybean residues) were used as raw materials by using cavitation jets technology to treat okara of bio-enzyme-made insoluble dietary fiber. X-ray diffraction and scanning electron microscopy were used to analyze the crystal structure and apparent morphological changes of okara insoluble dietary fiber under different cavitation jets treatment times (0min, 6min, 12min and 18min). Its physical, chemical and functional properties were characterized by particle size, water retention, swelling, apparent viscosity, glucose and cholesterol adsorption capacity. The effects of cavitation jets on their structure, function and adsorption characteristics were clarified. The results showed that the structure of the sample reduced particle size, crystallinity, and viscosity after 18min of cavitation jets treatment. The swelling force and water holding capacity reached the maximum values of (13.92±0.78)mL/g and (2.83±0.13)g/g at 12min, respectively. At this time, the adsorption capacity for glucose and cholesterol was the best. Therefore, cavitating jet can significantly promote the dissolvability of insoluble dietary fiber from okara and effectively improve its structure and physical and chemical properties, thus it can provide technical support and theoretical guidance for the high value application of soybean by-products.

Abstract:In order to clarify the effect of H2O2 treatment on energy metabolism, apoptosis and tenderization in the postmortem aging of Tibetan sheep meat. The Longissimus dorsi (LD) of Tibetan sheep was selected as the experimental material, Tibetan sheep meat was treated with distilled water, 50mmol/L and 200mmol/L reactive oxygen activator hydrogen peroxide, and then aged at 4℃ for 0d,0.5d,1d,3d,5d and 7d. The changes in ROS relative content, ATP content, Ca2+-ATPase and Na+/K+-ATPase activity, apoptosis rate, myofibril fragmentation index (MFI) and muscle fiber diameter during postmortem aging were tested. The results showed that after aging of 7d, the relative content of ROS in the 50mmol/L H2O2 treatment group was 39.19% significantly higher than that of the control group （P<0.05), the ATP content, Ca2+-ATPase and Na+/K+-ATPase activity and muscle fiber diameter of 50mmol/L and 200mmol/L H2O2 treatments were 25.73% and 25.77%, 38.39% and 36.70%, 27.30% and 29.46%, and 13.68% and 13.94% significantly lower than that of the control group (P<0.05), the apoptosis rate and MFI in the two treatment groups were 63.07% and 50.42%, and 5.74% and 5.19% significantly higher than that of the control group (P<0.05). To sum up, the H2O2 treatmented Tibetan sheep meat could promote ROS production, change energy metabolism of the Tibetan sheep meat, improve the activity of apoptosis, and promote the meat tenderization. It provided a theoretical basis for improving the quality of Tibetan sheep meat.

Abstract:The compilation of the load spectrum of tractor power output components is the main method to study the relationship between tractor working load and fatigue life. Based on the peak over threshold (POT) model, the time-domain extrapolation method of the tractor power take-off (PTO) operation load spectrum was studied. Aiming at the problems of complex calculation and low accuracy of the commonly used threshold selection methods for timedomain extrapolation, an automatic threshold selection method based on false discovery rate (FDR) was proposed. Based on this method, extrapolation of the tractor PTO torque load spectrum was realized. Firstly, a tractor PTO torque wireless acquisition system was developed to collect tractor PTO operating load data and perform data preprocessing;then, the FDR automatic threshold selection method was used to select the optimal threshold, and the upper threshold of the time-domain extrapolation data was determined to be 342N·m and the lower threshold was 100N·m. The maximum likelihood estimation was used to fit the scale parameters and shape parameters of the generalized Pareto distribution of excess threshold data, and the peak over threshold (POT) model of PTO torque load was built and compared with the fitting results of traditional image methods: the determination coefficients between the fitting results of the two methods and the load samples were greater than 0.995;from the perspective of goodness of fit, for the upper threshold, the goodness of fit of the automatic threshold selection method was 8.7% smaller than that of the image method, and for the lower threshold, the goodness of fit of the automatic threshold selection method was 31.21% smaller than that of the image method. Finally, the PTO load data was extrapolated based on the POT model, and 1 time extrapolated load data was compared with the original load time history;when the time domain extrapolation factor was 131 and the cumulative frequency of PTO torque reached 106 times, the PTO torque load spectrum was obtained. The extrapolated load spectrum was verified based on statistical characteristics and rainflow counting method. The results showed that the extrapolated load spectrum had the same distribution law as the original load spectrum, which can realize the twoway extrapolation of the average amplitude on the basis of retaining the load characteristics, providing a theoretical basis for the compilation of the tractor PTO torque load spectrum.

Abstract:When a tractor is working in the field, its key components are usually subjected to harsh geographical environment and weather conditions, moreover, the complex and variable loads, these make the key components of the tractor prone to fatigue failure. In order to accurately predict the fatigue life of large-horsepower tractors and provide theoretical basis for the design of key components of tractors, the stress field intensity method was adopted to analyze the fatigue life of steering drive axle housing of an 88kW tractor. The fatiguelife influencing factors, including stress concentration, size effect, surface quality and load characteristics were considered to optimize the traditional stress field intensity method. Traditional stress field intensity method was optimized from three aspects, i.e. the relative stress gradient, the fatigue damage area, and the measured load stress ratio, in order to obtain the modified S-N curve. The modified S-N curve was combined with the field measured load data to perform the fatigue life analysis of the steering drive axle housing, followed by comparing the prediction results of the optimizing stress field intensity method with the traditional method. The fatigue life of the dangerous point predicted by the optimizing method was 25467h，while the fatigue life obtained from the traditional method was 31860h. The comparison results showed that the optimized stress field intensity method was more accurate, since the perdition result was closer to the actual working life (24000h). The research result provided a more practical method for predicting the fatigue life of the key components of agricultural machinery equipment.

Abstract:Kinematic parameter error is the main factor which affects the absolute positioning accuracy of industrial robots. The accuracy of industrial robots can be effectively improved through error calibration. The completeness, continuity and redundancy of the kinematic model have great impacts on the identification accuracy of the kinematic parameters. To improve the accuracy of robot calibration and perform robot error compensation easily, a method of robot kinematic parameter calibration based on ZRM-MDH model transformation was presented. Firstly, the kinematic error model of the serial industrial robot TX60 was established based on the zero reference model (ZRM). The parameters of the ZRM modified OH mudel were identified with the measured pose error. Secondly, the ZRM was transformed into a MDH model through the method of circle point analysis. Totally fifty points were selected in the front workspace of robot TX60 for the kinematic parameter error calibration. The experimental results showed that the average comprehensive positioning error calibrated based on the MDH model was 0.081mm. The average comprehensive positioning error calibrated based on the ZRM-MDH model transformation was 0.062mm. To verify the stability of the calibration method, five areas were selected in the front workspace of robot TX60 for kinematic parameter error calibration. The experimental results showed that the calibration accuracy stability obtained based on ZRM-MDH model transformation was better. Therefore, the kinematics parameter calibration method proposed can effectively improve the accuracy of the robot calibration.

Abstract:Aiming at the problems in the existing design research of variable-degree-of-freedom mechanisms that mainly focused on topological synthesis and lacked of systematic research on its design process, the evolutionary theory was introduced to complete the design of variable-degree-of-freedom mechanisms. Firstly, according to the design requirements and predefined tree diagram structure, a “source” tree diagram model was established;secondly, evolution operations such as reproduction，crossing and mutation were used to expand the mechanism to obtain the tree diagram model of underdrive mechanism;finally, the tree model graph was transformed into mechanism movement diagram. The mechanism that met the axiom design criteria and motion requirements was obtained through screening and evaluation. Based on this, a prototype system for intelligent design of variable degree of freedom mechanism was constructed. The system included four modules of requirements analysis, evolutionary calculation, evaluation and screening, and database, which realized intelligent design. The realization process and programming steps of the system were used to illustrate the operation of each module. Taking the driving mechanism of picking machine as an example, the validity and rationality of the prototype system was verified.

Abstract:The punching mechanism was an efficient automatic forming equipment. There were many researches on the structural parameter’s optimization design, motion analysis and characteristics of the punching mechanism. However, there was relatively little dynamic analysis of the new punching mechanism. In order to expand the application of the punching mechanism, an optimized planar punching mechanism with one-DOF and two-slider output eight-bar was proposed. The mechanism had symbolic kinematics and contained three sub-kinematic chains (SKC), of which the inverse dynamic analysis was performed. Firstly, using the principle of structure coupling-reducing, a two-slider plane punching mechanism with zero coupling degree and single degree of freedom was optimized and designed. The topological structure was decomposed and the coupling degree of mechanism was declined to be zero (κ=0). Therefore, it was easier to obtain the symbolic forward position, velocity and acceleration. Then, the basic principle of the ordered single-open-chains (SOC) method based on the Newton-Euler principle, which was proposed by the author’s team, was introduced, the force analysis of each component was carried out. And the inverse dynamic modeling of the mechanism was established to obtain the dynamic supporting force and driving torque of the mechanism. Finally, the inverse dynamics modeling of punching mechanism was established by using the Lagrange equation, and the errors between the N-E’s SOC method and the Lagrange method were compared, which indicated that the former had high modeling accuracy. The research result provided a theoretical basis for the popularization and application of the SOC-method based on Newton-Euler in mechanisms that contained some SKCs and the strength analysis and dynamic performance optimization of the punching mechanism.

Abstract:In order to explore a new pipeline peristaltic mechanism and its moving mode, a new 3-URU pipeline peristaltic parallel mechanism with folding ability was proposed. Due to the spatial position relationship between the R and U of the branch motion of the mechanism can be equivalent to the Sarrus structure, the parallel mechanism can be folded, and its folding principle and folding interference were analyzed to obtain the folding ratio and verify its folding effect through simulation. The degree of freedom of the mechanism was calculated by using the screw theory, solving the positive solution of the position and through Monte Carlo method to get the working space of the agency. According to the position analysis, the creeping gait planning of the mechanism in the pipeline was carried out and the creeping gait in the pipeline was verified by the ADAMS software, the displacement curve of the two platforms and the curve of the branch chain rotation angle were obtained. It was comprehensively analyzed that the mechanism can carry out the peristaltic movement in the pipeline after expansion and had a small volume after folding, which provided a certain theoretical basis for the subsequent application of specific pipeline engineering.

Abstract:In order to meet the needs of verification and calibration of high-precision temperature measuring instruments, a high-precision thermostatic bath was designed. In terms of structural design, the inner cylinder structure of the thermostatic bath was optimized. The inner cylinder adopted an upper stirring structure, and the stirring chamber and the working chamber were separated. The upper and lower connecting structures were connected as a whole, and the rectifying grid and the working medium circulation system were designed to ensure the stable circulation and heat exchange of the working medium in the cylinder, and improve the uniformity and stability of the temperature field distribution. In the temperature control algorithm, the incremental proportion integration differentiation (PID) algorithm was used, the PID coefficients were self-tuning by the particle swarm algorithm, and the closed-loop negative feedback PID structure was used to achieve precise temperature control of the constant temperature bath. The selfdeveloped software was used to test the performance of the constant temperature bath. The test results showed that the designed constant temperature bath can realize a rapid cooling and heating process, and the temperature field stability at -10℃ was 0.0011℃/min, and the maximum temperature difference at the upper level in the working cavity was only 0.0034℃, the maximum temperature difference in the lower horizontal plane was 0.0020℃, and the maximum temperature difference in the inner cylinder working chamber was 0.0034℃. The entire system had a high degree of automation, stable temperature control, and can meet the national performance requirements.