Abstract:As the world’s most widely grown cereal crop, corn can be used as food, animal feed and industrial raw materials. Mechanized harvest of maize is an important link in maize production. Traditional artificial harvest requires a lot of manpower, material resources and time, and the corn harvest process is heavy, labor intensity is big, and harvest efficiency is low. In China, the phenomenon of rushing to harvest corn is common in the areas where the corn is harvested twice a year or more a year, so as to ensure the timely harvest of corn and the timely sowing of wheat, which is of great significance to realize the double harvest of corn and wheat production. With the accelerated development of maize industry, its mechanized harvest becomes more and more important. Corn mechanized harvest technology is one of the important ways to change the development mode of corn industry, improve the quality and efficiency, and enhance the competitiveness of the international market. At present, corn harvesting is in a key stage of transformation from mechanized ear harvesting to direct grain harvesting and transformation and upgrading of harvesting equipment. The research status and development trend of harvest technology and equipment at home and abroad were analyzed from two aspects of mechanized ear harvest and mechanized seed direct harvest, and the research progress of intelligent mechanical harvest technology such as automatic row and height automatic adjustment of cutting table, yield and moisture detection technology and automatic control of threshing parameters were analyzed. The standardization and mechanization testing methods and technical means were put forward, both to different requirements of high performance, high efficiency, high reliability model of agricultural equipment, enhance the level of intelligent equipment development train of thought, in order to promote China’s corn harvest mechanization technology progress and equipment upgrade to provide the reference.

Abstract:For citrus picking robot in natural environment, the accurate recognition and location vision system is one of the key factors ensuring the efficiency and safety of picking operations. In order to make the robot not only acquire the location information of the picking target accurately but also the surrounding obstacles, a novel obstacle recognition and location system based on Kinect V2 and improved you only look once (YOLO V3) algorithm was proposed. Firstly, five classification principles of citrus tree in natural orchard were defined, including one class that the fruit can be picked directly and four obstacle classes. Secondly, three maximum pooling layers were added to the convolution module of the YOLO V3 structure and K-means clustering analysis was conducted on anchor box to enhance the feature extraction performance of branches and leaves of the convolution neural network. Finally, threedimensional coordinates of the classification targets were obtained by using the Kinect V2 depth mapping to guide obstacleavoiding picking operation. The experimental results showed that the F-scores of obstacles and normal fruits were 83.6% and 91.9%, respectively, the positioning error was 5.9mm and the processing time of each frame was 0.4s, the success picking rate was 80.51% and success rate of obstacle avoidance was 75.79%. The research results provided a basis and guide for the picking path planning and obstacle avoidance of robotic harvesting task in natural scene.

Abstract:High speed and high precision is the development trend of maize precision planter，under the premise of ensuring the seeding quality of operation reached the national standard，the working speed of the existing pneumatic seed metering device can reach more than 12km/h，traditional maize precision planter mostly used land wheel to drive or measure speed，the phenomenon of land wheel slippage and chain hopping is easy to occur under highspeed operation conditions，so its difficult to ensure the seeding quality and exert the potential of the highspeed operation of the seed meter device. In addition, the maize precision planter only has a single function, and the sowing plant spacing is mostly adjusted by gear set, so it is not possible to achieve stepless adjustment of plant spacing. A maize precision planter monitoring system based on Android and CAN bus was designed, as the system was working, the speed of the planter was collected by GPS receiver, after parsing the speed information from the GPS signal, the master controller would send the driving motor speed adjustment instruction according to the seeding operation parameters, and the command transmission between the master controller and each seeding unit was completed by CAN bus. Human machine interaction through Android smart device can realize the functions of arbitrary expansion of seeding rows, realtime monitoring of planter position, online adjustment of seeding operation parameters, and realtime statistics of working area. In order to test the performance of the system, bench test and field test were carried out respectively. The bench test results showed that the systems humancomputer interaction function was normal, the position, speed and working area of the planter and rotation speed of the meter can be displayed correctly. The relative error of the metering drive motor speed regulation was less than 0.45%. Field comparison test with area measurement of eTrex209x handheld GPS+ Beidou binary receiver showed that the statistical average relative error of the operating area of the system was 0.81%, which was inferior to that of 0.29% of eTrex209x, and the measurement standard deviation was 0.06hm2, which was better than 0.11hm2 of eTrex209x; grounddriven seeding comparison test was carried out at plant spacing of 21cm, and five speeds of 4km/h, 6km/h, 8km/h, 10km/h and 12km/h， the test results showed that the quality of the two driving modes was decreased with the increase of the working speed, but the qualified index and distance variation coefficient of the system were less affected by the speed than ground wheel drive mode, when the operating speed reached 12km/h, the distance variation coefficient did not exceed 18.92%, and the qualified index was 90.05%, which was better than that of 22.17% and 83.25% of the ground wheel drive mode.

Abstract:A large amount of untreated straw is left in the field after rice harvest, which greatly hinders mechanized sowing and subsequent growth of wheat. The traditional solution is to bury the smashed straw in the soil or cover the surface of soil through multiple operations, which not only results in low efficiency and high energy consumption, but also hinders wheat germination due to excessive straw in the vicinity of the wheat seeds. In order to solve these problems, a planter with straw smashing and strip laying was provided, which adopted the clean area planting technique under full amount of straw and root stubble cropland. The planter was mainly composed of frame, straw crushing device, straw guiding device, rotary tilling device, seeding device and pressing device. In a single pass, the planter could complete multiple operation processes, including strawstubble smashing, seedbelt cleaning, interrow stacking, seedbed treatment, fertilization sowing, and soil covering and suppression. The key components such as straw crushing device, straw guiding device and rotary tilling device were theoretically analyzed to determine structures and parameters. In order to achieve high quality and smooth sowing of wheat in the seed belt, the sowing device and pressuring device were designed. Threefactor and threelevel orthogonal performance test was implemented by setting smashing spindle speed, radial distance and working speed as the influence factors, with straw cleaned rate and variable coefficient of the strip width as the evaluation index. Test results showed that when the smashing spindle speed was 2200r/min, radial distance was 20mm, working speed was 0.8m/s, and rotary speed was 300r/min, the average length of the straw was 110mm, straw crushed rate was 91.47%, straw cleaned rate was 92.58%, variable coefficient of the strip width was 10.91%, average sowing depth was 41mm, and seeding depth pass rate was 97.32%. Through tracking growth trend and measuring the yield, it was shown that there was no obvious shortage of seedlings and weak seedlings compared with the conventional seeding planter, and the yields of the two seeding planters were basically the same.

Abstract:Sweet potato is one of the most important food crops in China. Its total output is more than 100 million tons per year. It is a good fresh feed and silage. Sweet potato vines grow luxuriantly, crawl on the ground, crisscross and disorderly, sweet potato vines collection and treatment is a high labor cost work. In addition to the shortage of rural labor in recent years, the development of advanced technology and reliable performance of sweet potato vines recycling machine is of great significance to the development of sweet potato industry in China. It is very difficult to recover all sweet potato vines mechanically while they grow in furrows and furrows. Aiming at the problem, a kind of sweet potato vines recovery machine was designed. The machine can finish cutting, crushing, throwing and recycling of sweet potato vines at one time. The key parts of the machine were mainly designed and calculated, such as ridge copying knife roller mechanism and fan throwing device. The regression model between test factors and test indexes was established by response surface analysis. On this basis, the influence of test factors on test indexes was analyzed. The test factors were knife roller rotation speed, ground clearance and fan speed. The test indexes were crushing qualified rate, soil content and recovery of sweet potato vines. The test results showed that the optimal working parameters were as follows: knife roller rotation speed was 2070r/min, ground clearance was 16mm, fan rotation speed was 890r/min, corresponding crushing qualified rate of vines was 93.10%, soil content rate was 8.56%, and sweet potato vines recovery rate was 91.19%. The machine can cut and recycle most of the sweet potato vines in ridge and furrow, and improve the adaptability of seedling recycling operation. The research results provided a theoretical basis for structural optimization design and work performance improvement of sweet potato vines recovery machine, which was of great significance for realizing the mechanized recovery of sweet potato vines. Related models can refer to the research results.

Abstract:In order to solve the problem of high efficient cotton harvesting in China, a horizontal ingot picking head with highefficiency was developed. A parametric design mathematical model was established for the slot cam, which was the key working part of the picking head. The GUI module in Matlab was used to design humancomputer interaction visualization, generate groove cam curve, and imported it into UG to get threedimensional model and engineering drawings for manufacturing. By using ADAMS virtual prototype simulation software, the influence of the ratio of linear speed to working speed K on the trajectory of ingot picking was analyzed, and the reasonable range of K and working speed was obtained. Vector equation graphic method was used to analyze the matching relationship between the rotational speed of cottonremoval disc and the rotational speed of drum and ingot, and the corresponding range of rotational speed of cottonremoval disc was obtained. In order to verify the theoretical analysis and optimize the motion parameters of the highefficiency cotton picking head, the indoor comprehensive test bench for the cotton picking head was set up, and the forward speed, the spindle speed and the speed of the cotton disc were selected as the test factors, and the rate of inclusion and the rate of falling cotton were selected. The recovery rate and production efficiency were the test indicators. The second rotation orthogonal test method was used. Regression analysis and response surface analysis of the test results were carried out by using DesignExpert software, and the optimum parameters were obtained as follows: operation speed was 6.17km/h, spindle picking speed was 4272r/min, cotton disc speed was 2109r/min. Under the optimum combination of parameters, the impurity content was 8.78%, the knockdown cotton rate was 1.59%, the net recovery rate was 97.17%, and the production efficiency was 0.467hm2/h. Experiments were carried out under the same conditions. The average impurity content was 8.81%, the knockdown rate was 166%, the net recovery rate was 97.21%, and the production efficiency was 0.467hm2/h. The experimental results were basically consistent with the theoretical results.

Abstract:Aiming at the problems of manpower consumption and low picking efficiency of Hangzhou white chrysanthemum, a small and practical combtooth reciprocating device for Hangzhou white chrysanthemum picking was designed. According to the growth characteristics and picking abstracts of Hangzhou white chrysanthemum, the structure of the picking device was modeled by SolidWorks software, and the picking device was built. The device utilized comb stripping and rolling brushing cleaning functions of comb and the quick return characteristics of crankrocker mechanism to achieve the harvesting of Chrysanthemum morifolium. The physical and mechanical properties of Chrysanthemum morifolium were tested. In order to obtain the optimum working parameters of the device, field tests were carried out. Firstly, through single factor picking test, three main factors affecting picking effect were determined, namely, comb reciprocating frequency, comb spacing and comb working depth. Then, with comb reciprocating frequency, comb spacing and comb working depth as experimental factors, the orthogonal test was carried out to evaluate the picking rate, impurity content, breakage rate and landing rate of Chrysanthemum morifolium. Under the combination of reciprocating frequency of comb as 40r/min, distance of comb as 6.5mm and working depth of comb as 200mm, the picking effect was the best. At this time, the picking rate of Chrysanthemum morifolium was 92.47%, the impurity rate was 11.07%, the damage rate was 1.48%, and the landing rate was 1.41%. The comb reciprocating picking device of Hangzhou white chrysanthemum achieved good picking effect.

Abstract:Gravity separator is important equipment for seed separating processing. Aiming at that seed entering the separating table from disordered state to layered state occupied the area of the separation table, which affected the separating performance and made the adjustment more difficult, the prestratification technology of seed entering the separation table was put forward and the matching stratified feeding system was developed. The system was installed on the “5TZX-50 gravity separator”, which was located between the feed system and the separating table，and it was fixed with the separating table. It was mainly composed of a feeding hopper, a laminar grid, a guide board, a stratified net, and a wind tube. The feeding hopper received the seeds of the feeding system, and the stratified net transmitted the separating seeds to the separating table. The stratified feeding system was a vibrated feed machine and also a material suspender. The system vibrated synchronously with the separating table to complete the seed transport, while the system gave the seeds suspended air flow to stratify the seed. The key parameters obtained were: inclination angle of feeding hopper was 54°, laminar grid length was 8.5mm, width was 8.2mm, and stratified net inclination angle was 26°. The system model was established in SolidWorks, and the maximum acceleration was applied to the model by SolidWorks-Simplation, and the working stress and displacement were analyzed. The flow field was simulated by SolidWorks-Flow Simulation, and the flow velocity distribution, static pressure field distribution, and ion flow traces on flow field were obtained. Inclination angle of the guide board was optimized with the minimum entrance pressure as the optimization condition. Through the verification test, the Box-Behnken response analysis was carried out by the DesignExpert software，also using the variance analysis of the regression model，the mathematical regression model of performance indexes and influencing factors was established, the obtained best combination was flow velocity of 3.40m/s, vibration frequency of 9.75Hz and inclination angle of 29.65°. The results showed that range of errors of separating rate of impurity and target optimization predictive value was not more than 1.36%. The stratified feeding system can improve the impurity removal performance of gravity separator. For the low density impurity removal rate, the effect of airflow speed of stratified feeding system was greater than that of vibration frequency; for the high density impurity removal rate, the effect of vibration frequency of stratified feeding system was greater than that of airflow speed. The stratified feeding system reduced the influence of airflow speed on the high density impurity removal rate, and reduced the influence of vibration frequency on the low density impurity removal rate, thus reducing the difficulty of adjusting gravity separator. The research can provide theoretical basis and technical support for the design of stratified feeding system of gravity separator.

Abstract:To face challenges of unstable discharge for feed structure with equal diameter variable pitch screw, a theoretical analysis for variable pitch screw capacity was presented under some simplification assumption, which was compared with the equal pitch one, and resulted in a flow calculation formula by reasonable mathematical derivation. Furthermore, design of variable pitch screw was optimized by using response surface method which a flow rate was set as the response value in accordance with the above calculation formula. The BoxBehnken test analysis was performed with the help of DesignExpert software, turning out that the optimal pitch combination for the smallest target flow error was S1=0.35D, S2=0.60D, S3=0.85D, and S4=D. After all, movement simulation for millet as transport medium were performed in the screw with obtained optimum design, which the 3D model was built with SolidWorks software and the EDEM software was imported for discrete element analysis. After calibration, the result indicated that the simulative mass flow was 0.321kg/s, compared with calculation flow value of 0.319kg/s and the difference rate of 0.627%, relative error of which was measured to the actual required flow was 3.7%. The reliability of the flow formula was verified while the feasibility of the response surface method to design the screw structure was proven, which provided a reference for the design of the quantitative variable pitch screw. 

Abstract:In the wake of development of China’s agricultural aviation technology, the application of microplant protection unmanned aerial vehicles (UAVs) in the domain of crop pest and diseases management is becoming more and more extensive. There is no doubt that the UAVs have some significant advantages comparing with the traditional spraying methods because of its features of flexibility, environmental adaptability and high operational efficiency, particularly when working under complex scenarios that are inaccessible for conventional plant protection equipment. However, in practical applications, there are still some notable issues such as unsatisfactory application quality, low automation, and high safety risks which are limiting UAV’s working performance. Precision spraying technology and UAV autonomous control technology are the key factors in terms of ensuring the spraying quality, improving working efficiency and safeguarding flight safety. For the sake of endowing the UAV with some extent of autonomous flight capability, a multilayer control system was introduced, which consisted of a companion computer and an opensource flight controller that can communicate with each other via ROS and MAVROS. Meanwhile, an integrated method of external sensors (RTK-GPS and LiDAR sensor) and flight controller onboard sensors was proposed. This method can significantly improve the spatial position and control accuracy of the plant protection UAV. In order to further enhance the UAV’s autonomous flight ability, the task control system was designed and proposed, which enabled UAV autonomously flight between multiple task points with the horizontal and vertical location error of 0.145m and 0.053m, respectively. The research result effectively improved the plant protection UAV’s position accuracy and selfoperating performance, and provided some reference for the future development of precision spraying technology.

Abstract:Aiming at the problem of automatic control of walking speed of wheeled selfpropelled square baler, an automatic control system of walking speed of wheeled selfpropelled square baler based on workload feedback was designed. The working principle and control characteristics of the belt transmission CVT were analyzed. The overall design of the control system and design of hydraulic circuit were completed. The mathematical model of the transmission ratio and cylinder elongation of CVT was established. In order to balance the speed and stability of the vehicle speed adjustment, the control system program was divided into vehicle speed adjustment control and vehicle speed stability control, which ensured that the vehicle speed adjustment control program can achieve the target vehicle speed in a short time; the stability control program was designed to stabilize the speed of the vehicle under subsequent working conditions. For road loadincreasing and loadreducing test, the threshold value was 0.2km/h and the allowable deviation was 0.1km/h. The maximum dynamic deviation was less than 5%, and the maximum residual deviation was less than 1%. It took less than 5s for the speed to reach stability. The control system can automatically control the speed according to the simulated load of the machine, and can realize the switching between the automatic walking control and the manual walking control, which provided a certain reference value for the intelligent adjustment of walking speed of general chassis of wheeled selfwalking combine harvester.

Abstract:During the kiwifruit flower pollination, depositing sufficient pollen on the kiwifruit flower stigma is the key to ensure the yield and quality of kiwifruit. To improve kiwifruit pollination quality and reduce assumption of expensive pollen, kiwifruit flower pollination based on air-liquid nozzle spraying was studied. By means of an air-liquid pollination device, using the methods of droplets sedimentation weighing and image grayscale analysis, the effects of spray pressure, pollen suspension flow rate and spraying distance on the spray quality and distribution characteristics of pollination suspension were investigated to get optimum control parameters of spraying air pressure, pollen suspension flow rate and distance between nozzle and kiwifruit flower. Moreover, these control parameters were validated through kiwifruit flower pollination experiment on the scaffolding kiwifruit orchard. The test result showed that the air-liquid spray pollination method under these spray parameters can effectively improve the kiwifruit setting rate, the single kiwifruit weight and reduce kiwifruit deformity rate. The experiments showed that when applying the pollen liquid with mass fraction of 0.1% at petal fully opening stage, a single kiwifruit flower needed at least 42.9mg effective deposition of pollen liquid in the stigma area to pollinate all stigmas. Under the air pressure of 0.20MPa, the pollen liquid flow of 0.125L/min, the spray pulse duration of 0.1s and the spray distance of 35cm, the air-liquid spraying pollination can notably improve the effective deposition of pollen liquid on the stigma and the quality of the pollination. Pollination test on kiwifruit orchard showed that the kiwifruit setting rate and average kiwifruit weight as a pollination result of the air-liquid spraying pollination was 86.7% and 91.5g respectively, which was 13.4 percent points and 8.0g higher than that of the electric sprayer, respectively.

Abstract:Aiming to obtain deep insight into the cavitating flow characteristics inside an inducer, a series of experiments were performed to achieve the inducer macroscopic performance, cavitation development and the corresponding pressure fluctuation characteristics. It was found that the breakdown occurred earlier at larger flow coefficient, but there was one flow coefficient at which the inducer had the best cavitation performance. As the cavitation number decreased, the cavities occurred in the leakage vortex firstly, and the leakage vortex cavitation was connected with the shear cavitation in the leakage flow, forming a stable leakage cavitation. And the size of cavitation area was larger at smaller flow coefficients. The amplitude of pressure fluctuation near the tip was increased with the increase of cavitation area, and the blade passing frequency was the dominant frequency as the cavities were distributed symmetrically. As the cavitation number decreased further, several cavitation instabilities occurred, such as the backflow vortex cavitation and synchronous rotating cavitation, which would induce large amplitude pressure vibration and head degradation partially. The former one was developed from the leakage cavitation under heavy blade loading conditions, the later one was attributed to the unsymmetrical cavities without propagating in the circumference direction. With cavitation further development, axial flow instability occurred at larger flow coefficients, which was corresponding to the cavitation surge, and caused by the synchronous variation of the cavities on the three blades. When the cavities were stretched to the outlet of the inducer, the outlet flow angles were affected, the inducer lost the work ability, and cavitation breakdown occurred.

Abstract:Accurate prediction of maize maturity is of great significance for efficient harvesting and agricultural machinery dispatch management. In order to predict the maturity of maize at the regional scale in advance, the 4day MODIS leaf area index (LAI) product was used as the data source, selecting the corn in Heilongjiang, Jilin and Liaoning Provinces as the research object, combined with agricultural meteorological data and global multimodel ensemble forecast data. The dynamic prediction of maize maturity in Northeast China was carried out ten days in advance by using the accumulated temperature-solar radiation model and the LAI curve integral area model. The research showed that the prediction results of the integral area model of LAI curve were optimal in terms of time efficiency and precision. The coefficient of determination (R2) of the LAI curve integral area model was 0.87, the root mean square error (RMSE) was 2.5d, and it was effectively better than that of the accumulated temperature-solar radiation model. The current maturity prediction method had limitations such as low spatial resolution and poor prediction timeliness. It showed that the LAI curve integral area model method had applicability in the prediction of largearea crop maturity.

Abstract:Structurefrommotion with multiview stereo (SfM-MVS) facilitates the acquisition of photos by unmanned aerial vehicle (UAV) to generate the DEM and has been gradually applied to research on surface change processes such as channel erosion and slope erosion. Improving the DEM accuracy of the runoff plot is essential for investigating the interrill and rill erosion process by using SfM-MVS 3D reconstruction technology. The runoff plot 3D reconstruction process using Agisoft PhotoScan was optimized to improve DEM accuracy. The study was carried out in the bare land plot (4.5m wide and 20.0m long) at the Hailun Soil and Water Conservation Monitoring and Research Station of Chinese Academy of Sciences. Before the ploughing in spring of 2019, totally 7 and 30 measured points were evenly distributed around the outside and inside the plot, and the 3D coordinates were measured by using differential global positioning system (dGPS). Using the Phantom 4 Pro UAV, 42 photos covering the runoff plot were taken, and the shooting height was 3.5m. Each photo covered the width of the runoff and the UAV was gradually moved along the runoff to take the next photo. The results proved that potentially DEM accuracy improvement existed for the commercial SfM-MVS 3D reconstruction software by optimizing parameters. Within the PhotoScan, the appropriate processing setting parameters were determined by evaluating the image observation accuracy of the tie point and the ground measurement point, in order to weaken the DEM’s overfitting to the tie points or the ground measurement point. For the default parameter, the check point to control point error ratio was 1.95, and the optimized value was 1.26, which was reduced by 35%. Then, the camera models in PhotoScan were evaluated and the results showed that the 3rd model could reduce the error ratio between the check points and the control points, although the difference among different camera models was small. After the optimization process implementation, the DEM error was reduced by about 40%, both by point verification or point cloud verification method. Compared with the DEM error (20.0mm) under the default parameter setting, the optimized check point error was reduced to 11.0mm, which was similar with the rill erosion depth standard (depth greater than 10mm). Therefore, the optimized SfM-MVS 3D reconstruction process of runoff plot was more suitable for rill erosion research.

Abstract:Fast acquisition of soil salt content under vegetation cover is the objective need of saline soil management and utilization. Four kinds of croplands with different salinization values in Shahaoqu District of Hetao Irrigation Area were set as the study areas. The UAV equipped with a multispectral camera obtained the remote sensing image data of August, meanwhile, the soil salinity with depth of 0~40cm was tested. The sensitive band group, spectral index group and full variable group were introduced as model input variables. Four regression methods, including support vector machine (SVM), BP neural network (BPNN), random forest (RF) and multiple linear regression (MLR), were used to establish soil salinity inversion models which were based on three groups of input variables, respectively. Firstly, the model precision was evaluated, and then the effects of different input variables and different regression methods on the model precision were compared, finally the best salt inversion model was evaluated and optimized. The results indicated that comparing the R2 and RMSE of three variable groups, the spectral index group achieved the best inversion effect between the four regression model methods, and the sensitive band group and the full variable group had advantages and disadvantages in different regression algorithms. Between the four regression methods, the inversion accuracy of three machine learning regression algorithms was significantly higher than that of the MLR model. Moreover, both the sensitive band group and the full variable group in the MLR model showed the phenomenon of “overfitting”. And RF algorithm performed best between the three machine learning algorithms. Besides, SVM algorithm and BPNN algorithm performed better and worse in the model with different variable groups. The RF salt inversion model based on the spectral index group achieved the best inversion effect among the 12 models, the R2c and R2v reached 0.72 and 0.67, respectively, and the RMSEv error was only 0.112%. The research result can provide a theoretical reference for soil salinity monitoring in arid and semiarid areas.

Abstract:Improving the accuracy of salinization monitoring by satellite remote sensing plays a crucial role in salinization. A synthesized model for assessment of regional soil salinity was established based on UAV and GF-1 satellite remote sensing data. Applying the trend surface of the UAV data creation to the GF-1 satellite scale, through the improved TsHARP scale conversion method, after the conversion residual correction, the upscaling results were quantitatively and qualitatively analyzed. The results showed that the blue band B1, the nearinfrared band B5, the salt index SI, the salt index S5, and the improved spectral index NDVI-S1 had a good correlation with the measured soil salinity data in two remote sensing data. Correlation coefficients were more than 03. In the three regression models, the best model for monitoring soil salinization by UAV data was the SRU model, the optimal model of GF-1 data was the MLRS model. After upscale conversion, the inversion accuracy of soil salinity was much higher than that of direct satellite data inversion. The optimal model after ascending scale was obviously improved with the optimal model by directly using GF-1 data inversion, the former R2c was 0.338 higher than that of the latter, R2v was 0.369 higher, but RMSE was 0.057 percentge points lower. The research results can provide a reference for largescale rapid monitoring of salinization in the bare soil period of irrigation districts. 

Abstract:As the main cottonproducing province in China, Xinjiang has widely applied filmcovering technology. In the process of cotton mechanical picking, a large amount of film is also collected along with the seed cotton. If the film could not be thoroughly separated, it would be subsequently transformed into the ginned cotton together, which would reduce the quality of the textile. However, it is difficult to identify the film by using traditional methods, because the film is colorless and transparent without fluorescent effect. In order to detect the film covering the seed cotton, a novel algorithm was proposed based on shortwave nearinfrared hyperspectral imaging and deep learning. Firstly, considering the advantage of multichannel and model complexity, the variablewise weighted autoencoder was developed to weight hyperspectral image channel and transform them into lowdimension feature. Comparing with selecting one or deleting some channels directly, VW-AE was used to achieve information that was more useful and less influence on the negative feature. Then, several variablewise weighted autoencoders were stacked layer by layer to form deep networks, and a twolayer neural network combined with the BP algorithm was used to update the deep network weights. Next, the highlevel features from the deep network were set as the inputs of an extreme learning machine (ELM) whose parameters were determined by a particle swarm optimization method. Finally, the classification results of the ELM were merged into film and nonfilm two classes by morphology and connected domain technologies. Simulation experiments and a field test were carried out to evaluate the performance of the proposed algorithm. The results showed that the recognition rate of the presented algorithm was up to 95.5% and the separating rate of the film was 95%, which met the actual production requirements. 

Abstract:As the most essential pigment included in green plants, chlorophyll content is an important indicator in monitoring the growth of plants. The traditional spectrophotometric method used to measure the chlorophyll content of plant leaves has the advantages of high measurement accuracy and good repeatability, but the measurement process are timeconsuming. Therefore, developing a portable rapid chlorophyll detector with simple operation and low cost, and could be used insitu measurement will be appreciated by agricultural and forestry practitioners. Since the absorption peaks of chlorophyll a and b appeared at 660nm and 460nm, a portable chlorophyll content detector was developed based on this phenomenon. The hardware of the detector consisted of a single chip microcomputer, light sources module, light sensors, power module, and input and output module. The software, developed by using Keil C51 language, including main function and several subfunctions to detect the light, process data, calculate the chlorophyll content and display the values on screen. The leaves of spinach, big green vegetables and lettuce were used as samples, and the relationship between their chlorophyll contents and light absorbance at 460nm and 660nm were studied. It was found that the absorbance was increased with the increase of chlorophyll content, and their relationship could be described by first degree in two variable equation with the coefficient of determination of 080. The validation experiment on the developed detector showed that contrasted with the spectrophotometer method, the absolute measurement error of the developed detector was within the range of -0.32~0.20mg/g, and its mean absolute error was 0.14mg/g. Contrasted with the measured SPAD values by using SPAD-502 chlorophyll meter, the measurement error of the SPAD values measured by the developed detector was within the range of -3.3~1.8, and its mean absolute error was 1.1. Moreover, the measurement time of the detector was less than 2s and the cost of the detector was not high.

Abstract:Hyperspectral remote sensing is an important technology to fulfill realtime monitoring for crop growth status based on its superior performance in acquiring vegetation canopy information rapidly and nondestructively. The objectives were to test the accuracy, reliability and adaptability of the crop growth monitoring and diagnosis 402 (CGMD-402) in crop growth monitoring and application. The experiments were carried out during 2017—2018 at Experimental Bases Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang, China. The main promoted summer maize (Zea mays L.) varieties in the north Henan plain were chosen, and nitrogen treatments included five nitrogen fertilizer application rates (0kg/hm2, 75kg/hm2, 150kg/hm2, 225kg/hm2 and 300kg/hm2 pure nitrogen, expressed as N), the leaf area index (LAI), normalized differential vegetation index (NDVI), and ratio vegetation index (RVI) of different varieties and fertilizer treatments were monitored at jointing, bellbottom, tasseling, filling and maturity stages, respectively. The NDVI and RVI were monitored by different sensors of analytical spectral devices Field-spec Pro FR-2500 spectroradiometers (ASD FR-2500) or CGMD-402, respectively. NDVI or RVI characteristics were compared by ASD FR-2500 and CGMD-402, and analyzing the quantitative relationships of vegetation index between ASD FR-2500 and CGMD-402, respectively. Then, the LAI monitoring models of corn were constructed based on CGMD-402 NDVI and CGMD-402 RVI by using correlation analysis, regression analysis and other methods. The results showed that the canopy NDVI and RVI of corn were increased with the increase of nitrogen application rate in different growth stages, and the increase amplitude were 8.20%~36.59% and 4.40%~25.16%, respectively. The correlation coefficient (R) of NDVI or RVI based on ASD FR-2500 and CGMD-402 were 0.991 and 0.985, and the determination coefficient (R2) were 0.983 and 0.969, respectively. The results indicated that there was a highly consistent of vegetation indexes based on ASD FR-2500 and CGMD-402, and the NDVI and RVI from CGMD-402 were much better than ASD FR-2500. Monitoring models based on NDVI and RVI produced better estimation for LAI, and R2 were 0.911 and 0.898. Compared the predicted value with measured value to verify reliability and applicability of monitoring model, results showed that the R2 were 0.963 and 0.954, and the relative error (RE) of the measured value and predicted value were 6.65% and 9.37%, respectively. Therefore, it was suggested that the vegetation indices of NDVI and RVI by CGMD-402 was the most suitable model for monitoring corn LAI, and there was higher prediction precision, reliability and adaptability at different growth stages, and different N rates. The results indicated that the LAI from CGMD-402 was much better than that from AccuPARLP-80. These conclusions had important implications for monitoring crop growth by CGMD-402 in the the main corn producing area.

Abstract:As a part of the “two screens and three belts”, the northeast forest belt is a typical forest ecosystem in China. Studying its landscape pattern and changes is of great significance for assessing the ecological security of Northeast China. Based on the MODIS remote sensing images of 2000, 2005, 2010 and 2015, the landscape types of the northeast forest belt were divided into six categories: forest, grassland, wetland, farmland, artificial surface and other land use. The changes of landscape pattern in the northeast forest belt from 2000 to 2015 were analyzed in terms of ecosystem structure, direction of ecosystem transformation and landscape index. MCE-CA-Markov model was used to simulate the changing trend of landscape pattern in the northeast forest belt in 2020. The results showed that during the past 15 years, the ecosystem as a whole was in a stable state. The trend of ecosystem improvement in the first ten years was better, but the trend of change in the next five years was slower. The artificial surface was increased year by year, and the urbanization process was accelerated. The fragmentation of grassland was intensified in 15 years, while the degree of fragmentation of forests iwa low, and the changes in 15 years were small. It was feasible to use MCE-CA-Markov to simulate the evolution of landscape pattern. The results of the landscape pattern of the northeast forest belt in 2015 were consistent with the MODIS classification results. The Kappa coefficient was 0.9181 and the relative accuracy was 80.88%. The forecast results of the northeast forest belt in 2020 showed that the proportion of forests and farmland would further decline, and the proportion of grassland and artificial surface would further increase. The forecast results indicated that the ecological environment restoration and reconstruction in the northeast forest belt would face great pressure.

Abstract:The geostatistics and geographic information system (GIS) were used to analyze the corresponding changes of groundwater depth to landscape pattern evolution in Dengkou County, Bayannur City, Inner Mongolia. Firstly, the spatial heterogeneity of groundwater depth was analyzed by the semiofficial model in Dengkou County, and the spatial distribution of groundwater depth of the research area in 2008 and 2016 was simulated by using Kriging interpolation. Secondly, characteristics of the landscape pattern evolution of Dengkou County in 2008 and 2016 were studied. At last, the variation of groundwater depth and landscape pattern evolution data was coupled by GIS overlay analysis, and the response mechanism of groundwater depth to landscape pattern evolution was analyzed. The results were as follows: The groundwater depth of Dengkou County, in general, followed the trend of increasing from northeast to southwest, both the spatial heterogeneity and the random disturbance in the spatial heterogeneity were increased during the study period. The main landscape types of the county were forest land, followed by sandy land, bare land and water, the construction land and waters were increased obviously while arable land and sand land were decreased slightly during the study period. The evolution of landscape patterns had an obvious effect on the groundwater depth, construction land expansion deepened it, and the continuous improvement of the surface irrigation system made it rise. In general, the utilization efficiency of groundwater resources was obviously improved in Dengkou County. The study was of significance for the rational utilization of groundwater resources and the optimization of landscape pattern.

Abstract:Based on Landsat 8 multispectral imagery and ground survey data, taking the aboveground carbon density of arbor forest as the research object, the field survey data of aboveground carbon density of arbor forest, Landsat 8 multispectral image and DEM data were used to extract vegetation indices, texture features, principal component transformation factors, cap transformation factors and topographic factors as modeling variables. Pearson correlation coefficient method combined with residual mean square criterion method was used to screen variables. CoKriging interpolation and geographic weighted regression method were used to construct aboveground carbon density of arbor forest. And the estimated effect of the two methods were compared and analyzed. The results showed that the accuracy of the estimated model constructed by the geographic weighted regression method (R2 was 0.74, RMSE was 6.84t/hm2, MAE was 5.13t/hm2, RE was 0.74%), which was superior to the CoKriging interpolation method (R2 was 0.47, RMSE was 9.72t/hm2, MAE was 7.41t/hm2, RE was 012%), and the spatial heterogeneity of the estimated variables was well preserved (CVGWR=0.5372, CVCOK=04968), the geographic weighted regression method can obtain higher estimation accuracy. The research can provide a reference for estimating the aboveground carbon density of arbor forest and other forest parameters of forest at regional or large scale.

Abstract:To accurately inverse the forest height over a wider range, it is necessary to study the baseline selection method for multibaseline PolInSAR data to alleviate large errors in forest height inversion over a wider range from singlebaseline, exploring a better baseline selection method. The UAVSAR Lband multibaseline full PolInSAR data was used from five orbits obtained by JPL/NASA in Pongara, Gabon forest on February 27, 2016. Based on the maximum coherence difference (MCD) coherent optimization algorithm to make complex coherence maximum separation, the PROD method and the ECC method were improved, compared and analyzed; and verified by using the LiDAR data LVIS RH100 obtained by NASA on March 4, 2016. The error maps of the difference between the forest height and the LVIS RH100 inverted by the two baseline selection methods were plotted to analyze the results of the forest height inversion. And the density maps of the kz, canopy coherence amplitude corresponding to the two baseline selection methods and LVIS RH100 were plot to directly evaluate the difference between the ECC method and the PROD method selecting the baseline, and comparing and analyzing the pros and cons of the two baseline selection methods. Combined with these drawn graphs (forest height maps, error maps and density maps), the forest heights inverted by the two baseline selection methods were compared and analyzed. The error in low and high forest areas was large. The high forests were underestimated (the error was negative), and the forests in the low areas were overestimated (the error was positive). The underestimated or overestimation of the ECC method was greater than the PROD method, and the accuracy was inferior to the PROD method. The two methods had good consistency compared with the LVIS RH100 data. The linear equation of the ECC method and LVIS RH100 was y=0.50x+10.60 and R2=0.69. The fitted linear equation of the PROD method was y=0.63x+8.21 and R2=0.70. Validated with the LVIS RH100 data, the RMSEs of the ECC method and PROD method were 9.80m and 8.86m, respectively. The accuracy of the PROD method was improved by 9.63% than that of the ECC method. For low and high forest areas, when coherence separation or coherence amplitude between complex coherence was small, the degree of complex coherence separation and complex coherence amplitude were the main factor affecting forest height inversion. The PROD method comprehensively considered the complex coherence amplitude and coherence separation, so the PROD method was more suitable for inverting forest height than the ECC method. In mediumhigh forest areas, when the complex coherence was more dispersed and the coherence amplitude had not reached saturation, the straight line fitting effect of the coherence region was an important factor for forest height inversion. The ECC method was more suitable for inverting the forest height than the PROD method. The results demonstrated that the forest height inversed by the two baseline selection methods was consistent with LVIS RH100. The ECC method only took the linearity of the coherent region as the criterion. Because of taking into account the fitting effect and coherence amplitude of the coherent line, the forest height inverted by the PROD method was better and closer to the LVIS RH100 than the ECC method, alleviating the phenomenon that the ECC method underestimated tall forests and overestimated low forests to some extent. The PROD method was more suitable for forest height inversion in low and high forest areas, and the ECC method was more suitable for forest height inversion in moderately high forest areas.

Abstract:Phenotypic data analysis based on image data and machine learning has become one of the important issues in interdisciplinary research. In recent years, the big data and deep learning techniques have provided powerful tools for image analysis and machine vision. Currently, the generative adversarial network is becoming a novel framework for the process estimation generation model. It can generate highquality image data and provide an effective approach for solving the problem of small sample data and unbalanced data analysis and so on. As one of the important fungi, mushroom has a plenty of varieties and the long tail distribution and nonequilibrium of the data distribution bring great difficulties to its phenotypic intelligent classification and identification. Aiming to design a highefficiency mushroom phenotyperesistance network MPGAN with mushroom phenotype data. The phenotypic data generation technology of mushroom was studied, and the generated confrontation network structure for mushroom phenotypic data generation was designed. The system was divided into two modules: model training and phenotypic image generation. To improve the quality of the generation, Wasserstein distances and loss functions with gradient penalty were used. Experiments were conducted on two datasets: open source data and private data sets, and results analysis were performed with the learning rate, number of batches required to process EPOCH and Wasserstein distances. The phenotypic data of the mushroom produced with this approach can furnish data basis for the classification of the mushroom data in the later stage, and provide solutions for solving the issues of unbalanced data and long tail distribution of the mushroom classification. The research can provide technical support for the study of high quality mushroom phenotypic data sets.

Abstract:In the study of 3D plant phenotyping, with the efficiency of plant phenotype improving, 3D reconstruction technology has become a novel direction to obtain blade morphology at present. However, the traditional method of reconstructing 3D model such as laser scanner, structured light image and binocular stereo vision system is expensive and complicated. A new method extracting plant leaf area was proposed based on point cloud obtained by using structure from motion. Smart phone was used to get images of plants. Based on these images, the threedimensional point cloud of plants was reconstructed by using structure from motion algorithm. In order to restore the surface shape of the leaf, firstly, the noise of the leaf point cloud was removed by using threshold segmentation algorithm based on HSV color space. Secondly, the threedimensional coordinate matrix of point cloud was classified by using Kmeans clustering algorithm to segment single leaf point cloud by classifying. And then, the surface mesh model of the leaf was reconstructed by using the ball pivoting algorithm. At last, the leaf area was obtained by calculating the mesh area. To evaluate the proposed method, it was compared with the conventional leaf area measurement method. The average error of the calculation result of the proposed method was 4.67% compared with the measured value by using the scanning method, and the average error was 6.05% compared with the measured value by using the leafshape paper weighing method. In addition, the method of calculating leaf area by extracting contour by Canny edge detection algorithm was compared with the proposed method. The results showed that the proposed method required low cost and high precision, and met the requirements of nondestructive and accurate determination of plant leaf area.

Abstract:Aiming to realize the automatic identification of individual behaviors of breeders in this natural mating cage, reduce the systematic error of observation and sampling, analyze breeding chicken behavior and understand the health condition of chicken group, a realtime monitoring system based on nineaxis acceleration sensor and bluetooth wireless transmission was designed to automatically monitor and quantify the individual behavior of breeding chickens in this cage. Wavelet sym was used to reduce noise influence on original data preprocessing. According to the fluctuation of different behaviors acceleration curve, the characteristic value of acceleration data was extracted and the Kmeans clustering algorithm was used to identify behavioral features to obtain a stable clustering center. Distance comparison of acceleration sensor and performance test of portable battery and lithium battery as power supply equipment was done. Meanwhile, video was used to monitor and verify the five behaviors of breeding chickens. The system can quickly and continuously collect individual behavior information of breeding chickens in this cage, accurately identify individual feeding, drinking, fighting, mating and wingflapping behaviors. The average recognition accuracy of feeding was 94.31%, the average recognition accuracy of drinking was 92.53%, the average recognition accuracy of fighting was 84.03%, the average recognition accuracy of mating was 72.00%, and the average recognition accuracy of flapping was 92.31%. It was helpful to acquire individual behavior of breeding chickens without damage and quickly, and provide a scientific basis for optimal design and efficient management of facilities and equipment.

Abstract:In order to improve the accuracy of stunning state recognition of broiler chickens, a method of stunning state classification of broilers based on regional convolutional neural network (RCNN) was proposed. The following method was able to detect insufficiently appropriately and excessively stunned conditions of broilers. Initially, the image acquisition platform was utilized to collect the sample images. The data sets of collected samples were made according to the PASCAL visual object classes data set format. The total samples of 2319 images were randomly divided into training set and test set with the ratio of 6∶3. The augmented training sets were obtained through image enhancement technology. A Faster-〖JP〗RCNN was trained by using the augmented training set to detect the stunning states of broilers. The results showed that the recognition accuracy of the Faster-RCNN was 96.51% for 773 sample images in the test set. The accuracy of Faster-RCNN model was significantly higher than that of the established back propagation neural network (BP-NN) model (90.11%). The proposed model could be used to inspect the stunning state of more than 37000 broilers per hour. Deep learning technology was applied to recognize the stunning states of broilers, which can be used to automatically detect the stunning state of broilers and enhance automated slaughtering processes in the poultry industry. 

Abstract:In addition to reflecting its own health status, fish behavioral changes are also important in analyzing water quality. The accurate and rapid fish detection is the basis for behavioral change analysis. In order to solve the problem of low precision in the existing multitarget fish detection algorithms, a simple but effective multitarget fish detection algorithm was proposed. A new window scoring strategy was created to generate proposal windows by using the skeleton and edge cues of the fish image. The principal component analysis convolution kernels were trained to extract foreground and background features of fish images. The support vector machine was used to classify proposal windows to obtain windows where fish were located, and the nonmaximum suppression algorithm was used to eliminate redundant windows to complete the object detection. Experiments showed that the proposed algorithm based on the new window scoring strategy had a higher recall rate than the Edge Boxes algorithm, and the recall rate was up to 96.9% under the fixed proposals. The highest classification accuracy of proposal windows can reach 95.71%. By analyzing the missed detection rate, false detection rate and average detection time of the algorithm and Edge Boxes-PCANet, the overall performance of the algorithm was optimal. Using this detection algorithm, the multitarget fish detection can be achieved efficiently and accurately.

Abstract:In order to explore the suitable watersaving irrigation mode of rice, the cultivation experiment of rice under controlled irrigation and drainage conditions was conducted in the lysimeters from 2016 to 2017. The SWAP-WOFOST model was calibrated and validated against the data in observations of twoyear agricultural field experiment. And rainfall data of 60 years were grouped according to the type of hydrological years. At the same time, the irrigation and drainage module of SWAP model was improved to meet the local irrigation and drainage needs. The validated SWAP-WOFOST model was then used to simulate soil moisture transportation and rice growth process. By comparing the difference in field water management and rice yield under different hydrological year scenarios, the effects of four watersaving irrigation modes on reducing irrigation drainage and improving yield were analyzed. The results showed that watersaving irrigation technology could reduce the amount and frequencies of irrigation and drainage, reduce the physiological water demand of rice and field leakage, maintain high yield of rice and improve water use efficiency. During the 60year simulation period, the irrigation water productivity of controlled irrigation and drainage mode was 5.52kg/m3, 4.65kg/m3 and 383kg/m3, respectively for the wet year, normal year and dry year, which was the highest of four modes. Crop water productivity of controlled irrigation mode was 2.45kg/m3, 2.31kg/m3 and 2.06kg/m3, respectively for the wet year, normal year and dry year, which was the highest of four modes. Applying the entropy weighted TOPSIS model to evaluate and optimize the four modes of watersaving irrigation of rice, the results showed that controlled irrigation and drainage mode had a steady effect of watersaving and laborsaving on the premise of guaranteeing yield. Watersaving irrigation technology had guiding significance for irrigation and drainage practices in ricegrowing areas of Southern China.

Abstract:In order to explore the comprehensive regulation of growth, yield and quality of cherry tomatoes by multifactor coupling of water and fertilizer, the optimal combination of water and fertilizer factors was obtained. The orthogonal rotation combination design was carried out with the amounts of irrigation, applied N, applied K2O and applied CaO as test factors, and the multilevel fuzzy evaluation method was used to comprehensively evaluate the four factors and 14 subfactors for characterization of cherry tomatoes’ growth. Finally, the regulation model of water and fertilizer multifactor on the comprehensive growth of cherry tomatoes was constructed. The results showed that when other factors were at the intermediate level, the comprehensive growth of cherry tomatoes showed a linear relationship with the increase of irrigation amount. With the increase of N application rate, K2O application rate and CaO application rate, the parabolic relationship of the opening was downward. The interaction had a negative interaction effect between the amount of irrigation and the amount of K2O application and CaO application. Among the multifactor coupling effects, the irrigation, N, K2O and CaO coding value were 1.682, 0, -0.744 and -1.223, respectively, which was more conducive to the comprehensive growth of cherry tomatoes; for different levels of CaO application, the irrigation, N, K2O and CaO coding value were 1.682, 0.521, 0.071 and -1, respectively, which was most conducive to the comprehensive growth of cherry tomatoes. Finally, irrigation volume was the amount of water needed to replenish to the upper irrigation limit of 91.34%~100%, the amount of N applied was 12.26~13.50g/plant, the amount of applied K2O was 2.92~5.13g/plant, the amount of applied CaO was 2.69~4.39g/plant, the multilevel fuzzy comprehensive evaluation value had the optimal interval, which was most beneficial to the comprehensive growth of cherry tomatoes. 

Abstract:The highend intelligent greenhouses representing the highest level of modern facilities agriculture are mainly concentrated in developed countries. China has very few intelligent greenhouses with advanced levels, and they all rely on foreign imports. Not only are they costly, but they are also difficult to promote in large areas, which is not suitable for Chinas national conditions. However, China is still dominated by smallscale farmers, and more than 90% of the facilities are still simple plastic greenhouses. It is considered that wisdom cultivation cannot be realized. Therefore, in order to explore the artificial intelligence planting in the simple plastic strawberry greenhouse, according to the characteristics of the simple greenhouse in the Guanzhong area, the simple greenhouse Internet of Things (IoT) structure of the greenhouse environment perception, the computer background planting model intelligent disposal decision, the user mobile phone WeChat notification and the implementation of the inspection and evaluation were constructed. The system released the process list to the user based on the standardized technical system, and the disposal request was issued to each grower by the background computer based on the environment perception. The system obtained good results after being applied in the project implementation area. Single greenhouse yield and income were increased by 79.7% and 169.6% respectively, pesticide residues and inputs were decreased by 15.6% and 23.5%, respectively, saved irrigation by 29.8m3, water production efficiency and water economic efficiency were respectively increased by 137.6% and 255.4%.

Abstract:Field experiments were conducted to study the effects of different water and nitrogen managements on nitrogen remobilization efficiency during maize leaf senescence process. The experiment included three irrigation levels (150m3/hm2, 300m3/hm2 and 450m3/hm2) and four nitrogen application levels (0kg/hm2, 180kg/hm2, 220kg/hm2 and 260kg/hm2). The date of onset of leaf senescence, maximum rate of decrease in relative green leaf area, mean rate of decrease in relative green leaf area and the day of maximum rate of decrease in relative green leaf area under different water and nitrogen conditions were analyzed. The effects of nitrogen remobilization efficiency and grain filling process during leaf senescence process were also studied. The results showed that the date of onset of leaf senescence under each treatment occurred about 10 days after silking, and it was less affected by irrigation and nitrogen application. Under sufficient irrigation conditions, increasing nitrogen application could reduce maximum rate of decrease in relative green leaf area and prolong the day of maximum rate of decrease in relative green leaf area. With the same amount of nitrogen application, leaf nitrogen accumulation was increased first and then decreased with the increase of irrigation at silking stage. Within a certain range, leaf nitrogen remobilization efficiency was increased with the continuous increase of the day of maximum rate of decrease in relative green leaf area, and the maximum increment was 25.78 percentage points. The grain filling rate was slow first, and then fast, and eventually tended to change smoothly. The grain filling rate reached the maximum value after 30~40 days of silking. Delaying leaf senescence rate was helpful to improve 100grain quality. When the amount of irrigation was 300m3/hm2 and the nitrogen amount applied was 260kg/hm2, the day of maximum rate of decrease in relative green leaf area occurred at 48.90 days after silking and both leaf nitrogen remobilization efficiency and 100grain quality were the highest. 

Abstract:In order to study the effects of waterlogging and drought alternative patterns on fluorescence parameters and photosynthetic characteristics of rice at different growth stages under controlled irrigation and drainage condition, pit rice planting experiments were performed by taking farmland water level as the control index. Two alternative patterns of waterlogging and drought were set up: waterlogging after drought (HZL) and drought after waterlogging (LZH) in the tillering stage, the jointing and booting stage, the heading and flowering stage and the milky maturity stage. And the relative chlorophyll content (SPAD), fluorescence parameters and photosynthetic indicators were observed. The results showed that alternate waterlogging and drought stress reduced SPAD, and HZL treatment produced stronger inhibition. Fluorescence parameters and photosynthetic indicators of alternate waterlogging and drought stress, including the light energy conversion efficiency, photosynthetic quenching coefficient, maximum potential electron transfer rate, light saturation point, net photosynthetic rate, optional water use efficiency in the tillering stage and the jointing and booting stage can restore or even exceed the control level, in the flowering stage and milk stage had irreversible effects. Transpiration rate and stomatal conductance were inhibited respectively in the tillering stage and the milky stage, and promoted in the jointing stage. HZL treatment improved nonphotochemical quenching coefficient, and other major fluorescence parameters and photosynthetic index of HZL were lower than LZH treatment. LZH treatment in the tillering stage and jointing stage was more effective in compensating for photosynthesis. The inhibition of photosynthesis of HZL treatment in the heading and flowering stage was more obvious than that of LZH treatment. Therefore, alternate waterlogging and drought stress should be avoided in the late growth stage of rice, especially drought turned to waterlogging sharply.

Abstract:It is of great importance to prevent the quality deterioration of cultivatedlayer and reasonable improvement of the cultivatedlayer quality by analyzing the quality change characteristics, critical environmental impact factors and dividing the main obstacle types of the cultivatedlayer for red soil sloping land. Taking cultivatedlayer with typical planting system of the sloping farmland in red soil watershed as the research object, the quality change characteristics of the cultivatedlayer were analyzed by the method of cultivatedlayer quality index (CLQI), the obstacle types were defined and the key environmental factors’ function characteristics that affecting the cultivatedlayer quality were identified by the methods of clustering and principal component analysis, respectively. Results showed that the soil parameters characteristics of cultivatedlayer of red soil sloping farmland varied significantly, and the average thickness of cultivatedlayer was (18.88±3.17)cm, which was closely to the suitable thickness for crop growth. The soil saturated hydraulic conductivity was (3.39±2.6)mm/min, indicating that the soil infiltration and water holding capacity of the cultivatedlayer were slightly lower. The soil pH value was 5.06±0.64, which was faintly acidic. The soil with total nutrient content were poor and the quickacting nutrients were abundant. The cultivatedlayer quality was generally at a reasonable level of red soil watersheld, but existed degradation trend, with the reasonable degree (Ⅲ) of 72.2%, and unreasonable degree (Ⅳ) of 22.2%, and moderately degree (Ⅱ) of 5.6%. The CLQI of different slope positions was ordered as lower slope (0.482), upper slope(0.454) and middle slope (0.444). The cultivatedlayer types of red soil sloping farmland can be divided into six types of sticky knot type, nutrient infertility type, moisture restriction type, soil acidification type, no obstacle type and cultivatedlayer thinning type. Clay content, soil organic matter, soil saturated hydraulic conductivity, pH value and cultivatedlayer thickness can be used as diagnostic indicators of obstacle types for cultivatedlayer. The effective soil thickness, amount of fertilizer applied per unit sloping farmland area, slope direction and the tillage method were the key environmental factors which affected the cultivatedlayer quality of the red soil sloping farmland. Application of organic fertilizer, sub soiling and straw covering had obvious regulatory effects on improving cultivatedlayer quality. These results could provide useful parameter supports not only for the effective restoration and regular routes selection, but also for constructing the reasonable cultivatedlayer of red soil sloping farmland.

Abstract:In view of the current poor adaptability of soil dielectric calculation model, a bilinear dielectric calculation model was constructed based on bilinear theory. In order to explore the adaptability of the bilinear dielectric calculation model to soil dielectric measurement, seven different texture soils were selected for 0cm3/cm3, 5cm3/cm3, 10cm3/cm3, 15cm3/cm3, 20cm3/cm3, 25cm3/cm3 and 30cm3/cm3volumetric moisture content, dielectric spectrum measurement at different water contents in the frequency range from 1MHz to 3GHz. Through dielectric spectroscopy, the dielectric spectrum of the model can well reflect the dielectric properties of mixed media under different soil water contents; the ideal frequency point of soil moisture content dielectric measurement was 1.50288GHz. At the ideal frequency point, two empirical formulas for frequency domain measurement of soil moisture content were constructed based on the real part of complex permittivity and apparent dielectric constant. Through the analysis of experimental data, the calculated value of real moisture measurement formula of soil moisture content was compared with the measured value of the moisture content of the drying method. The R2 was 0.9126, RMSE was 0.0294cm3/cm3, and RPD was 3343. The soil water content was compared with the measured value of the dielectric measurement formula and the dry water content measurement. The R2 was 0.8907, which was better than that of the other three classic formulas (Topp formula, Roth formula and Malicki formula). The bilinear dielectric calculation model had a good adaptability to the calculation of soil dielectric constant. The frequency domain measurement formula of soil moisture content based on the model had high measurement accuracy for soil water content.

Abstract:In order to realize the estimation of soil heavy metal migration in largescale watersheds, the remote sensing information extraction technology plays an important role in such problems. A small and mediumsized watershed (the Daxia river basin) in Northwest China was sampled and investigated. By analyzing the soil and water samples of the Daxia river basin and its tributaries, the contents and distribution characteristics of heavy metals—As, Cd, Cr, Cu and Pb were determined. The contents of five heavy metals were ranked in descending order as the follows: Cr, As, Cu, Pb and Cd. According to the fractal characteristics of river network in river basins, a distributed migration model was put forward to estimate the heavy metal migration in the Daxia river basin. The fractal dimension of the Daxia river basin and its tributaries were extracted and corrected by using grid method and GIS technology. The fractal dimension of the water system was 1.0163, which was less than 1.6, and determined the geomorphology of the Daxia river basin in its infancy. The similarities of the watershed was modeled and verified. And based on the similarities between the fractal dimension and the runoff data, the subbasin runoff volume was further studied to estimate the distributed heavy metal migration, which was verified by comparing the estimated amounts of the SCS-CN model. The migration estimation results in heavy metals—As, Cd, Cr, Cu and Pb in the Daxia river basin were 0.6710t/a, 0.2099t/a, 6.2816t/a, 1.7465t/a and 1.8377t/a, respectively.

Abstract:Aiming to enhance the adsorption capacity of heavy metal onto lignocellulosic biomass and reveal the adsorption mechanism involved, wheat straw samples pretreated by hydrothermal, sulfuric acid, phosphoric acidhydrogen peroxide, microwave, alkali and alkalihydrogen peroxide, respectively were prepared. The changes in the microstructure, chemical components, and Pb2+ adsorption capacity were analyzed. The results revealed that the physicochemical pretreatments of wheat straw, such as hydrothermal, sulfuric acid, phosphoric acidhydrogen peroxide, microwave, alkali and alkalihydrogen peroxide, had different adsorption effects on Pb2+, and the adsorption capacity of Pb2+ was 1.01mg/g, 1.51mg/g, 3.99mg/g, 6.57mg/g, 9.56mg/g and 9.76mg/g, respectively. The hemicellulose can be removed effectively by the acidic conditions, lignin can be removed mainly by the alkaline conditions, and the degradation of lignin was further enhanced by the hydrogen peroxide, while microwave treatment was relatively weak. The quantitative analysis showed that the Pb2+ adsorption capacity was mainly positively correlated with content of hemicellulose, which can be described by the following equation Y=0.44X-0.09 (R2=0.83), and negatively correlated with content of lignin as the following linear relationships Y=-0.33X+12.95 (R2=0.81). Moreover, the importance of components to Pb2+ adsorption capacity in descending order was acidinsoluble lignin (AIL), hemicellulose, lignin, other components, cellulose and acidsoluble lignin (ASL). Thus, the content of hemicellulose was a key factor affecting the Pb2+ adsorption capacity of wheat straw. Therefore, the research result would help to establish the foundation for elucidating the quantitative correlations between lignocellulosic components and adsorption capacity and adsorption mechanism.

Abstract:Pecan is one of the top ten nuts in the world. Because of its good taste and rich nutrition, it is loved by people. But pecan is easy to deteriorate in the process of production and processing. Mistaken food can cause many hazards to human body. To solve this problem, a method for detecting the quality of pecans was proposed based on multifeature fusion and level set. Taking thinshelled pecans as research object, and the original image was preprocessed to solve the problem that the target object did not match the background area. The adaptive DRLSE method with improved edge indication function was used to segment the pecans in the image, and the statistical features of the gray histogram of the image were extracted. Multifeatures such as cooccurrence matrix, Tamura and local binary mode were combined and analyzed. The SVM discriminant model was established to realize the nondestructive quality detection of pecans. The experiment collected 200 normal, rancid pecans sample images, and subjected to image rancidity and multifeature analysis. The experimental results showed that the adaptive DRLSE segmentation method with improved edge indication function can complete the segmentation better than the traditional method even inside or outside the target. The accuracy of the method was as high as 96.15% in judging whether pecan was rancid or not, and on this basis, the average recognition rate was 90.81% in judging the degree of pecan rancidity.

Abstract:In order to rapidly and nondestructively detect the excessive soybean protein content in fresh minced chicken meat, hyperspectral images of three typical soybean protein adulterated in minced chicken meat were collected. Soybean protein flour (SPF), soybean protein concentrate (SPC) and soybean protein isolates (SPI) with 0~30% adulteration levels were mixed into the minced chicken meat. The hyperspectral images of prepared samples were collected in the visible and near infrared (400~1000nm) spectral range. The average spectra of regions of interest were extracted based on band operation and models based on the original and preprocessed spectra were established. Partial least squares regression (PLSR) quantitative models showed that the optimal performance were best for SPC (R2p=0.9984), followed by SPF, and then SPI. The twodimensional correlation spectrum (2DCOS) was used to extract the characteristic wavelengths to further establish multispectral models. The limits of detection (LODs) of the multispectral models were found to be 0.53%, 0.58% and 1.02% for SPF, SPC and SPI, respectively. The multispectral models above were also applied back to the raw multispectral images to visualize the distribution of different soybean proteins, as well as their adulteration levels. Results showed that the distribution differences within samples and among samples could be well visualized.

Abstract:Soy lipophilic protein (LP) and hydroxypropyl methylcellulose (HPMC) can form an LP-HPMC complex spontaneously after being combined under neutral conditions (pH value 7.4), but there are still some selfassembled LP and HPMC presented in solution. At present, the research still can not achieve the maximum compounding degree of LP-HPMC and the target of the most binding sites. Fluorescence and infrared spectroscopy were used to study the effects of different ultrahigh pressure treatments on the degree of LP-HPMC compounding and the force. The effects of ultrahigh pressure on structureactivity relationship of structural changes and functional properties of LP-HPMC were analyzed by emulsion activity and emulsion stability measurement, surface hydrophobicity analysis and dynamic light scattering particle size analysis. The results showed that the LP-HPMC complex was bound by noncovalent interaction and the secondary structure of the protein was changed accordingly. When the pressure was 400MPa, the maximum solubility and minimum surface hydrophobic value of the composite were 41.1% and 57.832, respectively, and the emulsification activity and emulsion stability index were also the best.

Abstract:The contribution of emulsion rheological properties to soy protein isolate and whey protein isolate mixtures O/W emulsion were investigated. The whey protein isolate (WPI) was taken as emulsifier. The effects of different protein mixing ratios and concentrations on the stability and rheological properties of the composite emulsions were investigated by particle size, Zeta potential, emulsification activity index (EAI), emulsification stability index (ESI), emulsion stability coefficient, scanning electron microscopy and rheology. The results showed that when the quality score of SPI-WPI emulsion protein was 2.0% and the SPI and WPI quality ratio was 1∶9, the average volume of the emulsion was 288.56nm, the absolute value of Zeta potential was 35.0mV, and the maximum emulsification activity index (EAI) value was 108.23m2/g. The maximum emulsification stability index (ESI) was 3.78471min, and the stability coefficient was up to 93.59%. Under this condition, the emulsion stability was the best. When the concentration of SPI-WPI emulsion protein was 20% and the ratio was 9〖DK〗∶1, the viscosity of the emulsion was the largest, the shear stress of the emulsion was the largest, and the rheological properties were the best. In summary, the addition of whey protein isolate increased the stability of the emulsion and reduced the viscosity and shear force of the emulsion. The research result provided theoretical guidance for the improvement and application of emulsion stability.

Abstract:Changing protein polymerization structure is an effective means to obtain different functional properties. The aggregation morphology of proteins is highly dependent on their composition and polymerization conditions. Thermal polymerization conditions with pH value below the isoelectric point will bring about great changes in polymerization structure of protein component. The research focused on the aggregate type of heatinduced gels from various components of soy proteins (soluble whole protein, 7S, 11S, acidic subunit, basic subunit) at low pH value（1.5~3.5）. The results of rheological properties, texture properties, polymer morphology and driving force for aggregation showed that most soy protein components formed finestranded gels at pH value of 20, and the aggregation of acidic subunit and 7S was more fibrillation. The particulate gel networks at pH value of 3.5 were composed of particles or clusters. Compared with particulate gels, the apparent viscosity of fine stranded gels was decreased significantly, with relatively low gel hardness and elasticity. The gelation property of 7S was the worst at the same pH value among different protein components. The main driving force of the fine stranded gels was hydrophobic interaction of noncovalent bond, and covalent disulfide bond for particulate gels. The relationship between the structure and functional properties of different soybean protein components at low pH value provided sound basis for different applications.

Abstract:In order to improve dynamical properties of the inwheel motor drive vehicle, a direct yawmoment control strategy with a hierarchical model was proposed. The upper layer of the strategy combined the feedforward control with the feedback control for motion tracking. More specifically, a feedforward controller based on wheel angle was designed to adjust the steadystate gain of yaw rate. Besides, the sliding mode condition integral controller was designed for feedback control, so that the yaw rate can track its expected value. The lower layer focused on the torque optimal distribution. According to the stability priority principle, the optimization function aiming at reducing the tire load rate of the vehicle was established, and the issue of torque optimal allocation was transformed into a quadratic programming problem to be solved. Based on a prototype of the 8×8 inwheel motor drive armored vehicle, a series of experiments were conducted. The experimental results showed that the proposed strategy can reduce the maximum error of yaw rate to less than 6% and 9% of the ideal yaw rate under continuous steering and double lane shifting conditions, respectively. Additionally, the proposed strategy was capable of achieving the decrease in tire load rate and the differential torque distribution of each inwheel motor. Thus the control effect was able to satisfy the requirements of continuous steering on high adhesion road and double shifting on low adhesion road. In conclusion, the proposed direct yaw moment control strategy with a hierarchical model was feasible to be applied to inwheel motor drive vehicles, and it can effectively improve the tracking ability and handling stability of the vehicle under various driving conditions.

Abstract:The electrohydraulic hybrid steering system (EHHS) was applied to the heavy vehicle, which had the steering angle tracking problem when working in the unmanned mode. In order to solve this problem and improve angle tracking performance, a complete nonlinear EHHS dynamic model was established, considering the parameter uncertainty and external disturbance. Based on the model, an adaptive doubleclosedloop angle tracking control strategy was proposed, in the external control loop, the parameter adaption law was designed to solve the parameter perturbation problem, and the improved sliding mode control strategy was used to calculate the desired steering torque; in the internal control loop, the PI controller was used to control the current of the steering motor to track the desired steering torque produced by the external control loop, and completed the steering operation finally. The EHHS model and proposed control strategy were built and simulated in Matlab/Simulink to compare the control effect of the new controller and the general linear controller. The results of the simulation verified that the control strategy proposed can effectively shorten the angle tracking response time of the EHHS system, which would remarkably reduce the steering angle tracking error under various operating conditions and ensured the tracking accuracy was not influenced when the system parameter was perturbed, and effectively improved the steering angle tracking performance of EHHS system in unmanned mode.

Abstract:Aiming at the problems of cavitation and positive and negative overshoot of pressure，caused by closed compression and closed expansion in the switching process of oil suction and discharge of axial piston pump, leading to flow pulsation, a 2D (twodimensional) pump was designed. The rotation of piston was used to distribute flow, and the distribution grooves of the piston were evenly distributed, which can eliminate the independent distribution mechanism and the effects of closed compression and closed expansion. In addition, two pistons in series were used to eliminate the structural flow pulsation caused by the motion law of piston. In order to further study the mechanism of flow fluctuation, mathematical models of instantaneous flow and pressure characteristics were established, the relationships between the structural parameters and the pressure in the cavity and the outlet flow were analyzed. It was concluded that the distribution area was the main factor affecting the flow fluctuation. Finally, the test bed was built, the prototype was made and tested. The experimental results showed that the volume efficiency of the prototype can reach 96% and the flow fluctuation was as low as 6.3%. It showed that the structure of the 2D pump can achieve high volume efficiency, and the flow fluctuation can be reduced by rotating flow distribution and double piston series connection. Damping groove in distribution windows also reduced flow ripple.

Abstract:A hybrid variable degree of freedom (DoF) branched chain was proposed after that moving platform of the parallel mechanism with 2R1T and 2T1R motion modes was connected with the planar parallelogram mechanism in series. The motion modes of series variable degree of freedom branched chains and hybrid variable degree of freedom branched chains were analyzed. A mechanism with series and hybrid variable DoF branching chains was constructed. The DoF characteristics of the parallel mechanism in the process of motion mode transformation were analyzed by using screw theory, and the rationality of the selection of driving pairs in different motion modes was verified. When calculating the degree of freedom of mechanism and selecting the reasonable driving pair of mechanism, different rods were selected as the moving platform to simplify the analysis process. The results showed that the mechanism with hybrid variable DoF kinematic chain had 3T, 2T1R and 2R1T motion modes. When the mechanism was in the general configuration of the three motion modes mentioned above, three driving pairs can control the mechanism. When the mechanism was transformed from 3T motion mode to 2T1R motion mode, it needed to pass through 3T1R instantaneous DoF configuration. When the mechanism was transformed from 2T1R motion mode to 2R1T motion mode, the mechanism needed to pass through 2R2T instantaneous DoF configuration. When the mechanism was in the transformation configuration which had 3T1R or 2R2T instantaneous DoF, the mechanism was in constrained singularity configuration, and an additional auxiliary driving pair was needed to control the mechanism. The mechanism can realize multiple motion modes by using fewer driving pairs. It did not need to reassemble the mechanism when the motion mode was changed.

Abstract:Aiming at the hand-eye calibration problem related to Eye-in-Hand vision and robot body of picking board, an optimization problem based on AX=ZB calibration equation was proposed. Firstly, the camera was driven by the robot to observe the calibration plate in multiple positions, and multiple calibration equations were obtained. One calibration equation AX=ZB was established by collecting data once. Kronecker product algorithm and the least square method were used to solve the calibration rotation matrix, and then the translation vector was solved according to rotation matrix and the least square method. In order to avoid the error transfer problem, the Jacobian matrix, the error function and Levenberg-Marquardt algorithm were established to optimize the attitude and position of the initial value simultaneously. Then, a simulation experiment platform was built in the ROS system, and the validity of calibration results was verified by the 3D visualization tool Rviz. The accuracy analysis of the calibration results showed that the accuracy of the new calibration method was increased by 30% on average compared with the traditional calibration method under different noise levels, and the accuracy of the new calibration method was increased by 31.1% on average compared with the traditional calibration method under different noise levels. Finally, the results of the grab test showed that the accuracy of the visual system was 39.2% higher than that of the transmission calibration method, and the success rate of the grab was 96.2%.