Abstract:Accurate perception in farmland environment is the premise to realize the obstacle avoiding in autonomous navigation for agricultural machinery. Stable and reliable environmental data acquisition system is the necessary condition for accurate perception. A point cloud acquisition system was designed for farmland environment based on LiDAR, which can realize the stable and reliable acquisition of farmland environment point cloud and the position and attitude of agricultural machinery. A multisensor data acquisition software was designed, which can achieve accurate and consistent global point cloud data acquisition. The system was composed of point cloud data acquisition module, vehicle position and posture acquisition module and data fusion module with tractor as mobile carrier. Among them, point cloud data acquisition module can acquire the point cloud data of surrounding environment and solve the problem of close blind area; vehicle position and posture acquisition module can acquire realtime agricultural machinery position and posture information; data fusion module can receive and integrate the environmental point cloud data and vehicle position and posture data, and then obtain the point cloud data after compensation. The system realized online collection of sensor data, time synchronization, spatial registration, data realtime display and storage. Point cloud acquisition experiments under farmland environment were carried out. The results showed that the acquisition system had good outdoor working stability. The online typical frame loss rate was not larger than 1%, while the offline typical frame loss rate was not larger than 0.47%, which can meet the requirements of farmland point cloud data acquisition. In order to analyze the data quality of point cloud collected by the system, the ground point clouds were filtered by straightpass filtering respectively by using the point cloud after compensation and the original point cloud. The results showed that the point cloud after compensation contained only a small amount of ground point cloud after filtering, which can be used as reliable data for obstacle avoidance in autonomous navigation of agricultural machinery.

Abstract:Fruit picking is an important part of agricultural production, and automatic agricultural fruit picking robots can improve work efficiency and work accuracy. An automatic fruit picking robot was developed, which mainly included automatic navigation system, picking system, motion system, control and power system. Among them, the automatic navigation system mainly included two parts: LiDAR navigation and GNSS navigation, which can be used to establish maps and plan the working paths. The picking system carried out fruit recognition through binocular stereo vision, and then sent the results to the actuator (manipulator), which included a sixdegreeoffreedom robot arm and twofinger end. The actuator then grasped the fruit stem and cut the grasping fruit stem to complete the fruit picking. The test results show that the designed and developed robot can complete indoor work through LiDAR navigation. The twofinger end effector that cut and grabbed the fruit stem can successfully finish the fruit picking job. And the developed computer control software can complete image acquisition, robotic arm control, road map creation and other operations. The results of LiDAR navigation accuracy test showed that the absolute error of navigation was less than 3.5cm at 1m/s driving speed, which can meet the demand of greenhouse fruit picking.

Abstract:Plant 3D information is an important parameter in the process of plant growth, reflecting the normal growth state of plants. In order to quickly and nondestructively acquire the 3D information of corn plants, a 3D information acquisition system based on mobile robot platform was designed. The fourwheeldrive robot was equipped with a lifting platform and Xtion camera at the end, and the Xtion camera acquired a 3D point cloud at multiple angles through the motion control of the robot. Firstly, the hardware structure and software platform of the acquisition system were described. Then, the steering of the fourwheel drive robot simplified the kinematics analysis. According to the system collection needs, the fourwheeled robot and the lifting platform controlled the camera to make a circular motion with a radius of R. The camera collected a 3D point cloud at interval of θ until the robot was moved for one circular motion. Finally, according to the transformation matrix 〖WTHX〗T〖WTBZ〗 for calculating the point cloud of the adjacent angle, the registration and splicing of the 3D point cloud were performed, and the filter was used for filtering to complete the 3D reconstruction of the corn. The watershed algorithm was used to mesh and segment the reconstructed maize plants, and the leaf length parameters were measured. The results showed that the motion error for mobile robots was less than 1cm. The error between predicted value and the true value of the leaf length of corn plant was between 1% and 5%. The system provided a new way for the collection of 3D information of corn plants.

Abstract:The application of agricultural robot is the inevitable trend of the development of intelligent agriculture. The main difficulties in the application of fruit picking robots are the difficulties of fruit recognition and location caused by fruit occlusion and uneven light. In order to solve the problem of inaccurate fruit recognitionin tomato picking robot, an image processing algorithm based on geometric morphology and iterative random circle was proposed, which can effectively segment and recognize the adhesive fruit in the image. Firstly, taking the tomato called Jiaxina as the research object, digital RGB camera was used to collect image. Then the image was preprocessed through Canny edge detection to obtain the fruit edge contour points. After obtaining the fruit edge contour points, the fruit contour points were obtained through geometric morphology processing, which can filter out nonfruit contour points. Finally, the fruit contour points were grouped and processed by iteration random circle, and the fruit recognition contour was obtained. The correct rate and accuracy rate of the algorithm was calculated after applying the proposed method on 80 images with 302 fruits. The results showed that the correct rate of fruit recognition was 85.1%, and the accuracy rate of fruit recognition was 79.1%. It was showed that the algorithm solved the problem of fruit segmentation in complex environments where multiple fruits were adhered or occluded by a small amount.

Abstract:Identification and location of maize canopy at seedling stage is an important basis for plant operation and fertilization. Aiming at the problem that fertilizer utilization rate is not high, strip and broadcast fertilizer are the main fertilization methods in China, a new method for identification and location of maize canopy in seedling stage was proposed, which could identify and locate the canopy centroid of maize in seedling stage under farmland environment. In order to locate the maize plant position quickly, the maize canopy was identified by the method of deep learning, and then the centroid location of the identified area was calculated. Firstly, Faster R-CNN was used to identify the seedling maize canopy in the field environment. Then the centroid detection algorithm based on the linear property of differential inner product improved the centroid location method of maize canopy. After the corn canopy and the weeds were segmented, the centroid of maize canopy was located and the pixel coordinates of maize seedling centroid were obtained. Through the verification in the actual farmland environment, the accuracy of identification and positioning of the canopy centroid reached 92.9%. The average detection time of a frame image was 0.17s. The positioning error of the canopy centroid was less than 1 pixel. Through the analysis of the test results, the research results can provide information support for the positioning fertilization and research basis for the followup variable fertilization operations, so as to achieve the production goal of saving fertilizer and increasing efficiency.

Abstract:In order to realize the dispatching management of multimachine cooperative navigation operation in dynamic farmland environment, the task planning of multimachine cooperative navigation operation based on ant colony algorithm was studied. The task planning of multimachine cooperative operation was divided into two parts: task allocation and task sequence planning. Firstly, a task allocation model of multimachine cooperative operation was established by combining global and local methods, considering both path cost and task execution ability. Then, by comparing and analyzing the task sequence planning problem and traveling salesman problem, the task sequence planning model of agricultural machinery operation was established by using ant colony algorithm. Finally, the simulation experiment of task sequence planning based on ant colony algorithm was carried out by using Matlab platform. According to the actual land information of Zhuozhou experimental farm, different groups of task sets were set to analyze the optimization path, the shortest and average distance of each generation and the fitness evolution curve of ant colony algorithm. The simulation results showed that the task sequence optimization based on ant colony algorithm can effectively reduce the cost of path and improve the efficiency of operation. The running time of the algorithm was less than 1 s, which preliminarily met the realtime requirements of multimachine cooperative operation, and provided a basis for further solving the multimachine cooperative navigation operation in the field environment.

Abstract:In order to improve the control precision of the autonomous navigation operation of agricultural machinery in farmland environment, three steering wheel turning control systems based on different types of motors were designed and developed. Based on the analysis of the parameters of the three kinds of motors, such as stepper motor, servo motor and stepper servo motor, and their performance differences, the tractor automatic steering actuator was designed, and equipped with industrial computer PC, PLC controller, front wheel angle detection mechanism and GNSS positioning system. The design and development of the industrial vehicle terminal software can realize the automatic navigation nested double closedloop control system and the corresponding PID control algorithm; the electrical schematic of the control system and the PLC steering program were designed. Finally, the tractor automatic navigation concrete pavement and field sowing test were carried out. When the tractor was linearly navigating at a speed of 0.8m/s, under the two test conditions, the root mean square errors of the stepper motor navigation system were 8.81cm and 12.09cm, respectively, the root mean square errors of the servo motor navigation system were 4.85cm and 10.55cm, respectively, and the root mean square errors of the stepper servo motor navigation system were 4.54cm and 5.53cm, respectively. The test results showed that the stepper servo motor had the best performance for steering wheel turning control in the automatic navigation system.

Abstract:Due to the complicated working environment in the field, the GPS data cannot accurately reflect the position of the seedlings during the automatic navigation of the rice transplanter, and may not meet the corresponding operational requirements. Therefore, in order to verify the accuracy of the satellite navigation operation of the rice transplanter, a method of using the total station to test the seedling path was proposed to verify the accuracy of the transplanter. By studying the data of the seedling point obtained by the singlepoint static measurement of the total station, the straightness of the seedling row and the parallel accuracy of the seedling rows were obtained, and the operation precision of the transplanter was evaluated. From the analysis of relevant data, the minimum and maximum values of the straightness of the seedling row were 3.08cm and 4.93cm, respectively, and the minimum and maximum root mean square error were 4.299cm and 6.263cm, respectively; the minimum and maximum values of parallel accuracy of seedling rows were 5.17cm and 15.53cm, respectively, and the minimum and maximum root mean square errors were 4.29cm and 5.43cm, respectively. The results showed that the method can achieve quantitative evaluation of the accuracy of automatic navigation operations in agricultural machinery fields.

Abstract:Accurate navigation of farmland operation carrier is an important condition to improve the efficiency of farmland operation in Xinjiang. At present, the commonly used map navigation software, such as Amap and Baidu Map, cannot achieve precise navigation in the farmland because of the accuracy of the map. Moreover, because of the vast area or the occlusion of sight in the farmland, it is difficult for the operation carrier to find the target point quickly. In view of this, a method was proposed to find the best field path by combining the Google Map API, the Amap API and the shortest path algorithm. Firstly, the boundary outline of the test field was depicted by Google Map API, and based on the location information provided by Amap API and Android application framework, the Xinjiang farmland navigation APP running on Android platform was designed and developed. The application effectively realized the precise navigation of different operating carriers such as human and ground machinery in Xinjiang cotton field, which was beneficial to agricultural technicians and operating vehicles in the path. In complex field environment, pest review and other operations such as spraying pesticides were of great significance to improve the efficiency of cotton pest control in Xinjiang.

Abstract:In order to study the dynamic model of laser grader in paddy field, improve the accuracy of control algorithm and optimize the design of mechanical structure, a dynamic measurement method of position and attitude parameters of grader based on monocular highspeed camera was proposed. Direct linear transformation (DLT) was used to establish the corresponding equations between monocular camera and local coordinate system, monocular camera and flat shovel platform respectively, and indirectly solve the transformation relationship between the two coordinate systems on flat shovel by Gauss-Newton iteration method, so as to realize the measurement of spatial position and attitude angle of flat shovel. Experiments were carried out with a monocular highspeed camera, and the data were compared with that of the attitude and heading reference system (AHRS) sensor. The experimental results showed that this method can measure the position and attitude parameters of the flat shovel. Compared with the AHRS sensor, the average absolute error of attitude angle was 0.687° and the standard deviation was 0.543°. The maximum absolute error occurred when the flat shovel was moved to a limit position of 3.76s, which was -1.92°. The measured position of the center of mass in the X, Y and Z axes was in accordance with the actual movement of the flat shovel. The motion condition provided a method for the simulation and verification of the dynamic model of the flat shovel.

Abstract:The structure and working principle of the chopping conveyor were analyzed, and the theoretical relationship between the angle of the incision, the length of the stem segment and the conveying efficiency of the crushed stem conveyor was established, and the theoretical values of the three were calculated. The change of working speed would cause the fluctuation of the corn stalk feeding amount, which had significant effects on the length of the cut section, the angle of the cut, and the transport efficiency of the broken stem. Taking the working speed as the test factor, the angle of the stem cut, the length of the cut section and the transport efficiency were taken as the test indicators. The field test was carried out, and the angle of the stem cut was calculated. The actual value of the length of the cut section and the conveying efficiency were calculated by data regression, and the mathematical model of the working speed and the angle of the cut, the length of the stem cut, and the actual value of the transport efficiency of the crushed stem conveyor were respectively obtained. Through the speed stalk chopping and conveying device, the model was actively designed to reduce the design deviation of the theoretical calculation model, and provided a reference for the design of the similar device based on the field conditions.

Abstract:Aiming to achieve accurate measurement of the feedrate, reduce the influence of the working environment and the vibration interference of the combine harvester on the measurement accuracy of the feedrate, improve the practical effect of the feedrate monitoring system, and study the feedrate signal processing method, including torque signal filtering noise reduction, GPS signal analysis and operation parameters calculation and speed signal calculation and interpolation, taking the feedrate signal in the field as a sample, the threshold signal was filtered by double threshold, and the prediction effect of different interpolation methods and the noise reduction effects of different filtering methods were compared. The experimental results showed that the piecewise linear interpolation and adaptive filtering were better than other methods in the sample range. The average error of feedrate prediction after data processing was 12.5%, which can meet the actual needs of combine harvester feedrate monitoring to a certain extent.

Abstract:When performing rice breeding, improved seed breeding, cultivation, and soil fertility tests in an agricultural community, quantitative seedling interpolation should be completed within the prescribed cell unit, and the required count value cannot be any error. When the rice transplanter is working, it is necessary to count the amount of transplanting. The transmission ratio of the drive shaft and the interpolation shaft can be used to obtain the number of inserted rows. At present, most of the transplanter insertion shafts cannot be installed on the shaft end of the counting device due to structural problems, and the counting device is difficult to fix. And the problem of missing meters due to inaccurate constraints has not been resolved. A design of a nonshaftend mounted counting device was presented based on the principle of precise constraint for the use of a rice transplanter. The sensor and the singletooth turntable were fixed on the clutch and the drive shaft respectively, and the counting device realized precise constraint on the rice transplanter, and compared the preset value of the counter with the actual number of inserted rows, it was verified that the counting device did not cause a leak. The test showed that the counting device can effectively achieve precise constraints in the narrow space on the mainstream rice transplanter models such as Jingguan and Kubota. The actual value was the same as the preset value, which proved that the counting device did not cause leakage when working.

Abstract:With the development of informatization, China has promoted “Internet+agricultural machinery operation”, which accelerated the promotion of agricultural machinery operation monitoring and the agricultural machinery monitoring & scheduling platform, and improved the quality and efficiency of agricultural machinery operation. Massive data of agricultural machinery operation was accumulated at the same time. In order to research on the relationship between the power of agricultural machinery and agricultural machinery in the process of subsoiling efficiency, the subsoiling operation data in Shandong Province was taken as the research object. In the research, totally 323 tractors installed subsoiling agricultural machinery remote intelligent monitoring terminal were selected as analysis samples (produced by Foton Lovol Corporation). The experiment data included 37981 sets about the operation area, duration of the operation and efficiency of agricultural machinery operation from April 2015 to April 2017. The agricultural machinery power range was from 52.20kW to 197.61kW. All the experiment data were divided into two parts: calibration set (80%) and validation set (20%) by sampling without replacement method. The agricultural machinery subsoiling efficiency model was established based on agricultural machinery power. The correlation coefficient between agricultural machinery power and subsoiling operation efficiency was 0.9147, and RMSE was 0.1684hm2/h. The RMSE of validation set was 0.3396hm2/h. The agricultural machinery subsoiling efficiency model provided a new method for agricultural machinery service organization to allocate agricultural machinery and assign tasks reasonably.

Abstract:Appropriate amount of fertilization in an accurate position is the basic requirement for meeting the precise fertilization operation in the field. The domestically used groundwheeldriven fertilizer applicator can not be controlled in real time to meet the requirements of fertilization accuracy. The outergroove wheel fertilizer has a simple structure to be controlled by a motor easily. In order to realize the positioning and fertilization operation based on the root position of the corn seedling stage, a position fertilization control system was designed based on a motor drive, which mainly included the master computer, the slave computer processor, the motor and the driver. The problem of fertilization lag was widespread in fertilization research, which was effectively solved. The factors causing the delay time were analyzed and the lag model of the fertilization position was established, the stability of the control system was improved and the error was reduced. A threshold control algorithm was proposed for placing the lag distance as the advance amount into the control system. The stability and accuracy of the control system were verified by a test platform. The test results show that the control system can control the speed of the fertilizing motor in the range of 50～201r/min stably, and complete the position fertilization with an average response time of 0.8s. The results of this study can reduce the delay of fertilization and improve the accuracy of fertilization positioning.

Abstract:In order to improve the situation of excessive use of fertilizer and low utilization of fertilizer, a variable rate fertilization control system based on matching fertilizer line and seed line was designed. The system was composed of four modules, including electric control module, hydraulic module, mechanical module and vehicle terminal. Firstly, automatic navigation system installed on the tractor was used for navigation fertilization and navigation sowing. According to the width of the mechanism and the location of the fertilizer (seed) tube, the navigation line was shifted to match seed fertilizer line，accurately. Subsequently, according to the target value, the actual rotation speed would be determined automatically by the corresponding strategy. The experiment results showed that the field matching error was within 6cm and more than 90% of the data points were within 3cm. The results of field experiment showed that the errors of the fertilizer value were less than 2.70% and the variation coefficient was less than 005 at the shallow layer. The errors of the fertilizer value were less than 7.95% and the variation coefficient was less than 0.08 at the deep layer. In the field experiment, traditional fertilizer application zone and reduction of 12% zone were set. The corresponding yield were 3900kg/hm2 and 3945kg/hm2 in 2018. Compared with the traditional fertilizer application, the yield of reduction of 12% zone had no significant difference. The variable rate fertilization control system based on matching fertilizer line and seed line provided a new method to reduce the excessive uses of fertilizer for wheat growth.

Abstract:Soil conductivity and soil mechanical resistance are the basis for determining the physical and chemical properties of soil, and soil texture and soil structure can be estimated. A vehiclemounted realtime measurement system based on the currentvoltage fourterminal method and the strain gauge bridge principle was designed and developed. It can be measured while plowing the ground, and can simultaneously measure soil conductivity and soil mechanical resistance. The system used a disc plow and a deep loose hook as the measuring sensor electrode. It had the functions of data measurement, transmission, display and storage, and had the characteristics of realtime, stability and accuracy. After the system was calibrated, the laboratory was tested in the laboratory. The conductivity part R2 was 0.9268〖JP〗 and the absolute error of the mechanical resistance part was 0.2~2.7N, indicating high accuracy and feasibility. The system needed to be further refined and improved through a large number of farmland experiments. It was hoped that more measuring sensor electrodes can be added later to determine more soil physical and chemical properties.

Abstract:In order to design and develop the field variable operation manure spreader based on crop realtime sensor, the research on the centrifugal disc spreader was carried out. Considering the structure and mechanism of the designed centrifugal disc spreader, the three key influence parameters were selected, which were spreading height, disc blade inclination angle and fertilizer drop position angle, and the influence on the uniformity of spreading of the double disc centrifugal spreader was analyzed. Based on the experiments designed by DesignExpert software, the response surface analysis tests and single factor orthogonal tests were carried out. Finally, the influence of various factors on the uniformity of spreading was analyzed. The response surface analysis test results showed that the magnitude of the influence of each factor on the distribution coefficient of variation was the blanking position angle, the spreading height, and the blade position angle after optimization of parameters, the fertilizer distribution coefficient of variation was the smallest, which was 9.95%, when the spreading height was 6880cm, the blanking position angle was 60°, and the blade position angle was 29.63°. The verification test showed that the average value of the predicted distribution coefficient of variation was 9.95%, the average value of the test value was 18.93%, and the average relative error was 47.26%. The results of single factor orthogonal test showed that under the single factor change, the distribution of fertilizer mass was changed obviously. When only the position of the blade was changed, the height of the spreading, the angle of the blanking, and the position angle of the blade were 70cm, 30° and 22.5°, respectively, the coefficient of variation of fertilizer distribution was the smallest, which was 22.87%. When only the height of spreading was changed, the height of spreading, the angle of the blanking, and the position angle of the blade were 80cm, 30° and 22.5°, respectively, the coefficient of variation of fertilizer distribution was the smallest， which was 1726%. When only the angle of the blanking was changed, the height of spreading, the angle of the blanking, and the position angle of the blade were 80cm, 60° and 22.5°, respectively, the coefficient of variation of fertilizer distribution was the smallest，which was 15.76%. Based on the test results, theoretical guidance can be provided for initialization system parameter setting of the variable fertilizer spreader, the improvement of the variable fertilizer application equipment, the performance optimization and the field operation.

Abstract:In order to implement the agricultural IoT systems of chlorophyll dynamic monitoring the function, a visiblenear infrared (660nm, 880nm) band spectral module was designed with the characteristics of small volume and low power consumption for the chlorophyll content detection in plants. Through AD conversion circuit, digital filter circuit was designed to get the blade reflected light digital signal. The reflectivity of reflected light signal was calibrated by gray scale plate, the R2 of the reflectivity correction model at 660nm and 880nm were 0.9996 and 09995, respectively. A total of 80 samples of 10 different grades were taken, and the chlorophyll content was detected by national standard method. The solution was poured into nonwoven cloth and measured by chlorophyll detection module. The normalized vegetation index (NDVI) value and soil and plant analyzer development (SPAD) value were obtained by the calculation of dual bands spectral reflectance, and the corresponding mathematical model was established to monitor the chlorophyll content. As a result, the determination coefficient R2 was 0.9557 and 0.9587, respectively. An experiment was conducted to establish the chlorophyll content monitoring model. After the spectrum signal measurement by chlorophyll detection module in the living plants nondestructively, the leaves were sampled and measured to get the true value of chlorophyll with the national standard method. According to NDVI and SPAD parameter, the correlation coefficient between the detection value and the true value was 0.8887 and 0.8745. Furthermore, an online dynamic monitoring experiment was conducted to monitor maize seedlings in the waterfertilizer stress group and the normal waterfertilizer management control group in real time. The chlorophyll changes in the plants were detected within 90h. Under the same management conditions, the chlorophyll change rules of plants were roughly the same. Under the influence of water and fertilizer stress, the chlorophyll concentration in the water and fertilizer stress group showed a downward trend. It was showed that the sensor system was feasible to monitor the chlorophyll dynamics of crops online and can provide support for crop information acquisition.

Abstract:In order to meet the demands of collecting and analyzing services of farmland crop information, a crop information collection and analysis tool was designed based on cloud service architecture with the combination of the smart phone terminal hardware, WeChat applet software and cloud service platform. The system mainly included Tencentcloud server module and mobile phone WeChat applet module. The MySQL was used to build a database for data storing, processing and download on cloud server. The WeChat applet was developed by CSS, JavaScript and applet packaged components. It was used to realize the interaction between collecting and uploading data and information feedback. In order to apply and test the system, the survey of biomass indication parameters of wheat in the field was taken as an example. The researches were carried out for the calculation of canopy coverage and plant rowspacing. More than 100 sampling images of wheat were captured at seedling period, and they were uploaded from applet to background and processing. After the image preprocessing following the target area detection by using Hough transform, image mask segmentation and image enhancement with erosion processing, the canopy of wheat was segmented and the canopy coverage was calculated by using HSV color space to highlight the pixel of sampling plants. The algorithms were proposed to extract the peak line of plant by projection and filtering method. Then, it was used to calculate the plant line spacing in the row. The linear regression model was established to indicate the fitting accuracy between the line spacing of the image recognition pixels and the measured values. The result showed that the modeling accuracy R2 reached 0.911. It could provide a technical support for crop information detection and investigation in the field.

Abstract:In order to rapidly and nondestructively measure the height of each vegetable seedling in plug tray grown in greenhouse, a method based on red, green, blueDepth (RGB-D) camera was proposed to extract height of each single vegetable seedling. Using Kinect fusion algorithms, RGB-D camera can create a canopy color 3D point cloud from the canopy color video stream and the depth video stream. 3D segmentation and identification of individual vegetable seedling from plug tray seedlings in the complicated natural scene was a key point to be resolved. Based on the principle of the RGB-D camera imaging, a method for calculating the height of each seedling in the plug tray was investigated. The procedure for processing topview color 3D point cloud of vegetable seedlings was proposed combining filtering and clustering for segmentation and identification of vegetable seedlings. The top view color 3D point cloud of bean sprouts were firstly filtered with the algorithm combined with the conditional removal and color clustering and statistical outlier removal to denoise the complicated natural scene points and noises. Individual seedlings were accurately segmented with the algorithm of Euclidean clustering. The results showed that the average measurement error of bean sprout seedling height was 2.30mm and the average relative error was 7.69%. This result can provide an effective reference solution for the extraction of the key growth parameters of seedlings. The proposed method could be used to quickly calculate the morphological parameters of each seedling and it was practical to use this approach for highthroughput seedling phenotyping. Compared with other stateofart segmentation methods, there was no need for this approach to create new training data and accompany annotated ground truth images.

Abstract:Obtaining yield distribution map of farmland and analyzing the spatial difference of plot yield are important foundations for implementing precision farming. In order to accurately collect the spatial difference of yield, and at the same time, the acquisition accuracy of yield monitor system and the interpolation accuracy of the output spatial distribution map are improved. The selfdeveloped realtime monitoring system of harvester was used. Based on accurate yield spatial distribution map, spatial variability analysis was conducted on wheat yield data from 2013 to 2015. Firstly, the results showed that the pretreatment method of threshold filtering can effectively eliminate outliers and restore the real yield distribution. Secondly, by comparing the RMSE values, it was determined that spatial distribution map of experimental plot yields drawn by the ordinary Kriging (OK) had higher interpolation accuracy. The minimum value of 826.70kg/hm2 appeared in the index mode of OK method for 2013, search strategy was elliptical, the largest adjacent element was 5, the smallest adjacent element was 3 and 1 sector. Finally, the curve parameters of semivariance function were used to obtain the spatial variability information of three seasons and optimal sampling interval of the system. The spatial variation of yield in 2013 and 2014 were entirely caused by spatial autocorrelation. And that of 2013 was mainly in the mesoscale range of 2~12m, and that of 2014 was in the mesoscale range of 2~5m. The spatial variation caused by random factors in the 2015 was 25%, which was in the small scale range below 2m. The spatial autocorrelation caused variation of 75%, which was in the mesoscale range of 2~15m. The sampling interval of the system should be kept at 2~10m. Too small or too large pitch was affected by large random factors or reduced interpolation accuracy. These results can be used to develop fine management decisions for farmland.

Abstract:In order to establish a better threedimensional point cloud morphological structure model of soil surface, a Kinect camera was used to obtain color images and depth images of the soil surface. For the traditional nearest point iterative algorithm in the point cloud registration, the space position requirements are more stringent, thus the method of initial registration of point cloud was proposed. Firstly, it was necessary to remove the useless background information and noise of the depth image of the acquired soil surface, and then initial registration and precise registration of the threedimensional point cloud were performed. In the initial registration process, the point cloud information of the acquired soil surface was normalized and aligned to the radial feature key point search to obtain representative and relatively uniform point cloud key points, and then the fast point feature value histogram method was used to extract the eigenvalues of the key points, and finally the random sampling consistency algorithm was used to purify the mapping relationship, thereby completing the initial registration of the point cloud. Finally, the nearest point iteration algorithm was used to accurately register the threedimensional point cloud on the soil surface. The registration time of the traditional nearest point iterative algorithm was 58.2s, the registration error was 3.80cm, the improved method registration time was 124.8s, and the registration error was 0.89cm. Compared with the traditional nearest point iterative algorithm, the registration time of the improved method was extended by 66.6s, but the registration error was reduced by 2.91cm. The results showed that the method was simple, easy to handle, and low in cost, and can realize threedimensional reconstruction of the soil surface.

Abstract:The panoramic camera can obtain image information, within the scope of agricultural machinery around 360° coverage, with a large coverage and other characteristics, more conducive to agricultural machinery automatic navigation and obstacle avoidance. However, it is necessary to conduct image mosaic and fusion of the images acquired by the multilens as the test platform, so as to generate panoramic images for the research on obstacle avoidance of agricultural machinery. With a Lovol tractor for test platform, with a panoramic camera, images of the farmland in the experimental field were obtained, first of all, image preprocessing, mainly for cylindrical projection transformation unified coordinate system, after the SIFT algorithm based on feature points extraction of image feature points and matching for traditional SIFT algorithm matching errors and problems affecting the quality of image matching, using RANSAC algorithm, several optimization iteration to eliminate the effect of error matching points, in view of the image transformation matrix matching generated after, to prevent the instability of its linear results and further optimization results, the nonlinear LM algorithm was used to refine the image, and then the linear weighted smoothing algorithm was used to fuse the image to achieve the generation of panoramic image. Calculated by using the experimental image overlap coefficient of correlation effect of quantitative evaluation of image mosaicking, and 30 groups of image, a total of 60 images were processed by RANSAC algorithm and the LM algorithm, and the experimental results showed that after RANSAC algorithm processing, matching point obviously by mistake, the average geometric distance between the matching feature points offset was decreased significantly, fell by an average of 39.4013 pixels to 0.5819 pixels, the correlation coefficient was from 0.2878 to 0.7249, compared with the traditional method of removing mismatched points by manually setting the matching threshold of SIFT algorithm, the correlation coefficient of 0.7249 processed by RANSAC algorithm was obviously larger than that of 0.5933 when the threshold was set at 0.4 and 0.2007 when the threshold was set at 0.6, which proved that this study can be applied to image mosaic in many cases and eliminate mismatched points; after LM algorithm processing, the average geometric distance offset was further decreased from 0.5819 pixels to 0.5693 pixels, and the correlation coefficient was further increased from 0.7249 to 0.7261, proving that this study can further optimize the transformation matrix and improve the mosaic quality of panoramic images.

Abstract:In order to better obtain the edge details and texture information of the image in the process of denoising, the balance was obtained in the removal and excessive smoothing of the image detail noise. A parameterize threshold function for denoising algorithm was proposed based on Bandelet transform, which took full advantage of the multiscale characteristics of Bandelet transform and the geometric characteristics of image.The image was decomposed by using stationary wavelet with translation invariance to overcome the oscillation of the image and the threshold was estimated by Birge-Massart strategy. Then the optimal geometric flow direction was obtained by minimizing Lagrange function. The quadtree of Bandelet transform was optimized according to minimum mean square error (MSE) principle. Finally, the adaptive Bayesshrink parameterized threshold function was used to image denoising. The results showed that the proposed method performed more effectively to preserve the edge features and the fine structure of the denoising image. Compared with other methods, the peak signaltonoise ratio (PSNR) and structural similarity (SSIM) obtained by the proposed algorithm showed that the performance of noise reduction was improved significantly. Therefore, the parameterized threshold function denoising algorithm based on Bandelet transform was feasible and effective in image denoising of locust slices, which provided technical support for its subsequent processing.

Abstract:Images collection of farmland is one of the important components of modern agricultural informatization. From the images, information such as the growth and distribution of crops and pests in the field can be monitored. Foggy weather is a special natural weather phenomenon. When the image of farmland is collected, fog is often caused, resulting in blurred and faded images. Aiming at this problem, based on the dark channel prior, a Shannon-Cosine wavelet precise integration method for farmland images dehazing was proposed. Aiming at the problems of the block effects in the transmission image and the loss of image texture after restoration, the transmission image was refined by the proposed algorithm. According to the characteristics of the transmission image, the nonlinear partial differential equation model was used to smooth and preserve the edge of images. The multiscale Shannon-Cosine wavelet was used to discretize the equations. In this process, Shannon-Cosine wavelet can adaptively select feature points and identify the image texture to highlight the image texture features. This process reduced the size of the equations and the amount of computation. Then the precise integration method was used to solve the equations, and this method also effectively improved the calculation accuracy. The proposed algorithm also improved the atmospheric value A and improved the operation speed. The experimental results showed that the transmission image obtained by the algorithm had clear boundaries and was locally smooth. The recovered image had better definition and richer texture than the original algorithm. Compared with the original dark channel prior algorithm, the proposed algorithm increased the ratio of newly visible edges by 30.36%, the contrast by 40.72%, and the standard deviation by 28.21%. The proposed algorithm had better dehazing results.

Abstract:In order to monitor the chlorophyll content of crop leaves quickly and without damage, a portable chlorophyll content monitoring device was designed with active light. Dual wavelengths were involved with the deep absorption of crop at 660nm and strong reflection at 850nm. After the conversion, modulation and amplification of the signal, the signal indicated the reflected light intensity of crop was obtained. The calculated models of spectral reflectance at dual wavelengths were fitted by using the calibration with four gray board, and the R2 were 0.993, 0.979 at 660nm and 850nm, respectively. The stability and the antiinterference tests of the light source were carried out. The results showed that the mean square errors of the stability test data at 660nm and 850nm were 0.0079 and 0.0044, and the 〖JP2〗error rates were 2378% and 1223%, respectively. The mean variances of antiinterference were 0.0099 and 0.0187, the error rates were 2.000% and 4.360%, respectively. The correlation test of chlorophyll gradient and dualwavelength reflectance was designed. The results showed that the correlation coefficients of dualwavelength and chlorophyll concentration based on 660nm and 850nm were -0.919 and 0.272, which reflected the strong absorption characteristics of chlorophyll in the vicinity of 660nm. Meanwhile, 850nm was selected as reference band witha low correlation coefficient between reflected light and chlorophyll. Furthermore, the field experiment was conducted to detect the chlorophyll content of maize canopy. The vegetation indices were calculated, including NDVI, DVI, RVI, SAVI dualwavelength spectral vegetation index. The correlative coefficients r of NDVI, DVI, RVI, SAVI and SPAD were 0.892, 0.846, 0.867 and 0.883, respectively. The linear models of NDVI, DVI, RVI and SAVI with SPAD were established, and the model determination coefficient R2 was 0.831. Subsequently, the model embedding function provided by the device can be used to further improve its field application ability in crop chlorophyll content detection.

Abstract:Chlorophyll is an important indicator for the evaluation of plant photosynthesis ability and growth status. In order to obtain the spatial distribution of chlorophyll content in field crops quickly and nondestructively, the chlorophyll content detection and distribution map drawing method of maize canopy were carried out based on UAV remote sensing technology. Firstly, the aerial images of 150 maize plots were collected by UAV mounted camera and spliced by Pix4dmapper software. Totally 80 maize leaves were sampled in the experimental field. They were processed following chemical extraction and spectrophotometer measurement to obtain the chlorophyll content value. The images and chlorophyll data were used to form the underlying data source. In the aspects of data processing, the position and orientation system (POS) data of the sample points were matched with the images of the UAV using ArcGIS software. For the RGB images captured by the drone, the threechannel component values of R, G and B were firstly extracted. The color feature parameters were calculated such as greenred difference, normalized redgreen difference, super green, and so on. In addition, six kinds of texture features were calculated, including mean, standard deviation, smoothness and thirdorder moment. The error back propagation neural network was used to build chlorophyll detection model for maize canopy leaves. The experimental results were as follows: the root mean square error (RMSE) of the maize canopy chlorophyll content detecting model based on BP neural network was 4.4659mg/L, and the coefficient of determination R2 was 0.7246. The chlorophyll content of each pixel in the field canopy image was calculated. The visual distribution map of chlorophyll content in field maize canopy was drawn based on pseudocolor technique. The chlorophyll content distribution map of field maize canopy could be used to visually distinguish the field road and canopy area, showing the difference in chlorophyll distribution of the plot. By nondestructively detecting the chlorophyll content and chlorophyll distribution of canopy corn canopy, it could provide a support for field crop growth evaluation and precision management.

Abstract:Crop transpiration was the main driving force of substrate water transfer. Aiming to establish a greenhouse tomato crop transpiration estimation model and prediction model based on the change of substrate water content, and make a comparative analysis.The calibrated EC5 matrix moisture content sensor was used to record the realtime change of matrix moisture content after the first irrigation and before the second irrigation. Realtime crop transpiration was measured by weighing method. The estimation model of daily transpiration per plant of tomato was established by multiple linear regression calculation of variation of substrate moisture content and volume of substrate cultivation tank. Taking the variation of substrate moisture content, air temperature, air humidity and illuminate intensity as input, the prediction model of daily transpiration per plant of tomato was established by GABP neural network algorithm. The greenhouse crop transpiration estimation model and predictive model were tested respectively with the greenhouse crop’s daily transpiration by linear regression analysis, the results showed that the prediction accuracy of the estimation model based on the variation of water content in the matrix was 0.9729 and 0.9796, respectively, in the seedling stage and florescence, and the prediction accuracy of the prediction model was 0.9915 and 0.9890, respectively. The differences between the two was not big, but the estimate model operation speed was much higher than predictionmodel of operation speed. In practical application, the estimation model had good robustness to environmental changes, and the relative error was less than 5% at seedling stage and flowering stage. The estimation model had the value of popularization and application for greenhouse irrigation management.

Abstract:The subject of this experiment was Hg2+, Hg2+ was detected by L cysteinemodified CdTe quantum dot solution, and Hg2+ was combined with the surfacemodified quantum dots to cause fluorescence quenching of quantum dots, Hg2+ concentration and fluorescence. The intensity of annihilation had a linear relationship. This linear relationship was used to quantitatively detect the Hg2+ content in the solution by quantum dots. The detection band was 400~800nm.The detection limit of the method under optimized conditions was 6.11×10-9mol/L (S/N=3, n=11), and the linear range was 9×10-9~5×10-6mol/L. The deviation (RSD) was 2.86%. In a certain experimental environment, the variable normalization (SNV) preprocessing of the characteristic band and the partial least squares (PLS) modeling analysis can obtain better selfprediction ability and actual prediction ability, and the determination coefficient of the correction set reached 0.8784. The standard deviation was 11.6313μmol/L, the determination coefficient of the validation set was 0.7287, and the standard deviation was 18.7174μmol/L. The results showed that the modeling effect was good, the operation was simple and convenient, the experiment was fast, reliable and nonpolluting, which indicated that the method of detecting Hg2+ by quantum dot fluorescent probe was feasible.

Abstract:A nutrient solution recipe analysis method was developed based on biplot method. The difference of 〖JP2〗five recipes containing seven nutrient elements, including NO3N, K+, Ca2+, Mg2+, PO4P,〖JP〗 NH4N, SO4S was discussed. The result revealed NO3N, K+, Ca2+ got a better performance than other elements in terms of the difference and content. Meanwhile, Yamazaki nutrient solution recipe and Cornell nutrient solution recipe independently hold a high correlation, respectively. A unified modeling samples collection training support vector machine (SVM) model was established for the detection of NO3N, K+ and Ca2+. Compared with the prediction model without difference analysis as input, the mean relative error of NO3N, K+, Ca2+ was decreased from 7.66%, 11.88% and 11.55% to 6.41%, 6.14% and 10.20%, respectively, in the Yamazaki recipe environment. While in the Cornell formula environment, the mean relative error was reduced by 1.79 percentage points, 2.98 percentage points and 1.13 percentage points, respectively.

Abstract:In order to promote the application of modern information technology in the field of management of salinealkali land and get an effective mean for evaluating improved effect on soil, a realtime information collection and control system was designed, including online underwater pipe monitoring subsystem, field microclimate monitoring subsystem, pest diagnosis subsystem, crop growth monitoring subsystem, and automation irrigation subsystem. And ZigBee multihop adhoc network was constructed for field data transmission. Based on the regional characteristics of Dongying Agricultural High Technology Zone, a cost function was defined in the comprehensive consideration of maximum network connectivity, robustness and timelines. And then, a global optimization deployment scheme for ZigBee network nodes was proposed with the maximum connectivity of the network. Meanwhile, a local data collection software with LabVIEW and the remote cloud service platform under Brower/Server structure were all achieved. The communication between local control software and remote server was achieved through TCP/IP, where the communication reliability was guaranteed by the mechanism of offline detection and reconnection. A large amount of monitoring data can be accumulated in the remote server, providing data support for the treatment of saline soil in the future. The system was put into use, the test showed that the system met the functional requirements of data monitoring and automation control for irrigation and fertilization, where monitoring data can be shown on the website in realtime and control commands can reach the correct actuator through the website quickly. Meanwhile, the communication test showed that the packet loss rate of the designed ZigBee network data transmission was less than 1%.

Abstract:Soil total nitrogen (STN) content is an important parameter to ensure the normal growth of crops and measure the nutrient abundance of soil, and it is also the most important indicator for variable fertilization decision of crops in base fertilizer period. In order to establish a set of rapid online measurement method for soil total nitrogen content, a vehiclemounted soil total nitrogen detection system software was developed based on Windows. The design of the detection system included architecture design and function design. The MySQL database management software was used as the database for development and design, and the data interaction between the detection system software and the MySQL database was realized; the software function design mainly included the main interface design of the detection system, the design of data acquisition and analysis interface, and the trajectory mapping interface design of the soil data detection points based on HTML5 geolocation technology. When the vehiclemounted soil total nitrogen detection system performed soil detection, soil spectral data at different sensitive wavelengths were collected by the software of detection system, and soil total nitrogen content was obtained by inversion of the soil total nitrogen content prediction model embedded in the software of detection system. Simultaneously, the acquired GPS information was used to generate the trajectory map of the soil data detection points through the HTML5 geolocation technology. Finally, the software of detection system was tested. The test results showed that the vehiclemounted soil total nitrogen rapid detection system software could effectively collect and display soil spectral information at different sensitive wavelengths, soil total nitrogen content and GPS information, and accurately generate the trajectory map of soil data detection points. It proved the reliability and stability of the software working of vehiclemounted soil total nitrogen rapid detection system based on Windows. It could meet the needs of field online rapid detection of soil total nitrogen content and generation of soil data detection points trajectory map.

Abstract:Nitrogen in the soil is a key element in crop growth. The development of nitrogen spatial distribution mapping system not only reflects the distribution of nitrogen in the soil intuitively, but also provides technical support for variable fertilization decisions. Based on the .NET platform, written in C#, the GIS functions such as data file type conversion，interpolation analysis and raster layer rendering were realized by ArcGIS Engine, and based on soil spectral data obtained by nearinfrared spectroscopy, the system made by Python can predict the N values through a BP neural network regression model. The integration of ArcGIS Engine and Python was achieved by adopting C# command line. The system was tested with the fiftyfour soil samples which were collected by the vehiclemounted soil parameter sensor. The results showed that the system can estimate the nitrogen content corresponding to the soil sample based on the spectral data. Both inverse distance weighted and trend surface interpolation can generate the nitrogen spatial distribution map combined with the geographical coordinates of the collection points. The IDW interpolation correlation coefficient reached 0753 which satisfied the precision requirements. And through the IDW spatial distribution map, the place where nitrogen was concentrated can be identified obviously. Through the TS spatial distribution map, the whole trend of nitrogen distribution can be mastered. In summary, the spatial distribution maps made by system can provide a theoretical basis for the formulation of corresponding variable fertilization decisions.

Abstract:LoRa technology is a lowpower LAN wireless standard, which is increasingly used in the agricultural internet of things due to its advantages of low power consumption and long communication distance. LoRa nodes can not only be equipped with sensors for environmental monitoring but also figure out received signal strength indication (RSSI) of each node by checking the quality of received data packets. RSSI value has a strong relationship with signal transmission intensity, communication distance and communication media. A farmland internet of things system was designed and developed based on LoRa technology, and the relationship between LoRa communication quality, transmission power, communication distance and sugarcane plant population density was preliminarily explored, and the feasibility of using RSSI value to predict sugarcane population density was evaluated. In the communication experiment, a total of 3000 RSSI data were collected at 20 different distances in the open field and two sugarcane fields. The results showed that under the transmission power of 15dB, the communication quality on sunny days was more stable than that of 10dB, 20dB, rain, dew and other humidity, and the sugarcane density obviously affected the communication quality. The main factors affecting the communication quality in sugarcane field were analyzed based on these data, and the model of LoRa signal strength and communication distance was preliminarily established, the tests showed that its R2 reached 0.9291. The results showed that the system designed can provide important guidance for sugarcane growth monitoring, management and yield prediction.

Abstract:The agricultural internet of things has been gradually popularized in agricultural planting management, but most of the application scenarios are mainly focusing on guiding growers to fertilize and irrigate crops from its acquired field data. Environmental and meteorological data of the crop’s entire growth season can also reflect the microscopic and macroscopic environment of crop growth, and hence influencing the final yield. Therefore, the environmental data (soil moisture content and soil temperature) and meteorological data (precipitation and air temperature) of a sugarcane field collected from a field IoTs system were used to build up yield predict models. The collected data was ranged from the year of 2008 to 2017, and the original BP neural network and an improved BP neural network based on genetic algorithm (GA-BP) were adopted to predict the yield. Comparing the prediction results of these two models, it was found that the prediction accuracy of the GA-BP neural network model was significantly higher than that of the BP neural network model, with its R2 reaching 0.9894, and its average relative prediction error was only 0.64%, which was also obviously lower than that of BP neural network model (5.66%). The result indicated that the GA-BP neural network was effective and feasible in predicting sugarcane production.

Abstract:Obtaining farmland information quickly and efficiently is the basis of precision agriculture.“Currentvoltage” fourterminal method and time domain reflectrometry (TDR) are two main methods for contact measurement of soil conductivity. A soil conductivity instrument based on the principle of “currentvoltage” fourterminal method was designed, which was calibrated and compared with TDR to explore the applicability of the two methods. The calibration results showed that when the electrical conductivity was in the range of 0 and 14mS/cm, the determination coefficient R2 reached 0.960, and the instrument had high accuracy in this range. The results of soil contrast test showed that the fourterminal method and TDR had good linearity under the condition of low salt content and water content, but when the salt content of sandy loam was more than 0.6%, the change of conductivity measured by fourterminal method tended to be smooth. When the moisture content of silty clay was 20% and 25%, the measurement data of the two instruments were basically the same, so it was needed to try best to avoid measuring when the soil moisture content was high. The comparative experiments under different texture soils showed that the fourterminal method and TDR were greatly affected by the soil type, and the higher the clay content of the soil was, the smaller the conductivity of the soil was. During the test, the fourterminal conductivity meter can measure in real time, but the time of each TDR measurement was about 20s, which was not conducive to realtime measurement.

Abstract:Moisture content is the most direct and necessary index to analyze and judge agricultural drought, realize watersaving irrigation and hydrological forecast. In order to grasp the information of soil moisture content in time and accurately, a soil moisture monitoring system based on narrow band internet of things (NB-IoT) was designed. Circular cylinder was adopted by the probe of the system. The moisture content of soil was measured by the output frequency of the frequency divider in the probe, and the capacitive soil moisture sensor was calibrated. The calibration experiment of the sensor showed that the calibration curve adopted the cubic polynomial curve of the output frequency of the sensor, and the correlation was good, and the determination coefficient can reach 0.9851. The influence of environmental temperature change on the measurement accuracy of soil moisture content was analyzed with a thermostat, and the temperature change of 1℃ and the frequency division of the probe was 0.1386kHz, a first order linear equation was obtained to compensate the influence of ambient temperature on the frequency output of the sensor. Soil moisture content was measured at 50.4~65.5℃ and -18.0~23.6℃. The results show that the system can run stably and reliably at high temperature and low temperature.

Abstract:In order to improve the detection accuracy of cow mastitis, an automatic eye and breast location method was proposed by using thermal infrared imaging technology to measure the temperature of key parts of cow. The gray scale histogram of the thermal infrared image of dairy cows was firstly analyzed, and then the HSV color features and skeleton features in the threshold segmentation images were extracted. Then, the eye position of dairy cows was automatically detected based on the HSV (Hue, Saturation, Value), and the skeleton feature vector was calculated, which was used to classify and automatically detect the breast position by the support vector machine. In order to verify the effectiveness of the positioning algorithm, totally 40 randomly selected naturally walking cows were verified. The test results showed that the positioning algorithm proposed could effectively locate the eyes and breasts of cows, and the accuracy of video frame recognition within the positioning error of 20 pixels was 68.67%. The cow eyes obtained according to the positioning algorithm was carried out on the temperature difference value of breast milk cow mastitis test, rating by temperature threshold and degree of dairy cow mastitis morbidity and somatic cell count method, comparing the test results it was showed that the rating 1 detection accuracy was 33.3%, the rating 2 detection accuracy was 87.5%. The results of this study can accurately obtain the position and temperature of the eyes and breast under the natural walking condition.

Abstract:The body surface temperature of pigs is an objective reflection of their own physiological conditions. Testing the body surface temperature of individuals and groups is an important way to achieve fine and efficient production of pigs. In order to realize the online monitoring of the body surface temperature of pigs in a top view, a method of detecting and tracking the head and trunk of pigs based on thermal infrared video was proposed. Firstly, the pig channel in the collected thermal infrared frame was intercepted, and the overall skeleton of the pig was extracted after pretreatment in this area. Then the key points at the front end of the skeleton were scanned to detect the head skeleton. After that, the trunk detection was realized by calculating the position of the key point of the torso tracking frame based on the position of the head and the spatial position tracking frame of the body. Finally, head and body detection were performed on each frame to achieve head and torso tracking. Using the collected 50 pig videos, the proposed algorithm was tested on the Matlab R2014a platform and compared with the compressive tracking (CT), kernel correlation filter (KCF) and fast discriminative scale space tracking (FDSST). The results showed that the tracking precision was 0.6752 (threshold value was 20 pixels), which were 9.41, 7.09 and 2.72 percentage points higher than those of CT, KCF and FDSST, respectively. The proposed algorithm can effectively solve the problem of automatic detection and tracking of the head and trunk of the thermal infrared video of the pig in the top view, and can provide more accurate regional information for the body temperature extraction of the head and the trunk. The average tracking frame rate was 31.63f/s, which can meet the requirements of online monitoring of farms. This technology provided technical support for further intelligent monitoring equipment for postgraduate pig body surface temperatures.

Abstract:The mounting behavior of pigs is generally manifested as a pig puts two front legs on the body or head of another pig which stays lying or dodged quickly. Mounting between pigs often causes epidermal wounds and even fractures, which reduces animal welfare and affects the economic benefits. Therefore, it is necessary to isolate the mounting pigs in time. In view of the low degree of automation of current mounting behavior detection of pigs, an algorithm based on Mask R-CNN was proposed to recognize the mounting behavior of pigs. Firstly, the top view videos of pigs were shot, and the dataset labels were made by Labelme. The transfer learning was applied to train the ResNet-FPN network to obtain the pig segmentation result and extract the mask pixel area in each sample. The value of the minimum mask pixel area in each sample was extracted in order to build an empirical sample set for mounting behavior recognition, and the discriminant threshold of the mounting behavior of pigs was determined. In the experiment, the test dataset was used to evaluate the pig segmentation network model and the mounting behavior recognition algorithm. The segmentation accuracy of the network was 94%, and the accuracy of the mounting behavior recognition algorithm was 94.5%. The experimental results showed that the algorithm can effectively detect the mounting behavior of pigs and provide support for livestock breeding automation.

Abstract:The cow body size is an important indicator for evaluating the health status of the cow, and the extraction of body measurement point is critical in the body measurement process. In order to solve the problem of automatic extraction of the measuring point of the cows point cloud, a method for automatic extraction of the measuring point of the back of the cow was proposed. Firstly, a cow deep video capture platform for data acquisition was built, the original point cloud data of the cows back collected by the Kinect camera was preprocessed to remove the surrounding complex background. Secondly, the principal component analysis method was used to calculate the local plane normal vector and curvature, the point cloud of the cow back was streamlined, the noise points and redundant points were removed, and the characteristic points of the cows back ridge and boundary contour were preserved. Finally, according to the geometric characteristics of the measuring point of the back of the cow and the spatial structure relationship between the measuring points, the body point measurement data of the cow’s back point cloud data were automatically extracted. Based on the complete back depth video of 33 cows, ten frames of each cow were selected for a total of 330 frames of experimental data, and the average absolute error of all body measurement points extracted by this method was less than 1.17cm, which can meet the requirements of measurement error of the back of the cow in the livestock application. Compared with the traditional streamlined method, the extraction time of the body measuring point after the simplified method was shortened by 33.72%. The results of this study can provide technical support for automatic measurement of cow body size.

Abstract:In order to solve the problem that it is not easy to accurately and automatically locate the hoof position of dairy cows in the process of lameness detection by machine vision technology, a method of hoof location for dairy cows was proposed. Through extraction of cow hoof image by preprocessing cow image in visible video, study on spatialtemporal characteristics of dairy cows walking, analysis of temporal and spatial variations of cows hooves in images, a spatiotemporal difference algorithm was proposed， the lowest coordinates of connected domain was computed, and the cows’ hoofs were located accurately. Through analysis of the moving sequence of cows’ hoofs, extraction and classification of homologous hoof position data, the data requirement of the lameness track detection method was met to judge the lameness of dairy cows by using the hoof position of the same side of the cow before and after the lameness track detection method. The positioning test of cattle hoof and lameness test were carried out; this method can accurately locate the cows’ hoofs, when the threshold was 20 pixels the accuracy was 73.8%, the average error of calculating the landing position of cows’ hoofs reached 11.3 pixels, the accuracy of cow lameness track detection was 93.3%, the accuracy of lameness claudication was 77.8%, the results can accurately locate the hoofs of dairy cows under natural walking conditions, and realize automatic detection of lameness in dairy cows.

Abstract:In order to detect pigs accurately and quickly in complex environments, a multitarget pigs detection algorithm based on improved convolutional neural network (CNN) was proposed. Twolevel linear SVM was trained to generate highquality candidate regions by using binarized normed gradients (BING) of pig images. The improved CNN model was used to classify and identify candidate regions. Finally, the nonmaximum suppression (NMS) algorithm was used to eliminate redundant windows. The proposed algorithm reduced the number of training samples and parameters. Through the experiment of CNN network structure and parameter optimization, the efficiency of network training and the effect of target detection were analyzed. Experiments showed that compared with the traditional CNN model, the improved CNN model had shorter training time, faster convergence speed and stronger robustness. The classification accuracy of foreground and background of pig images was 96%, which was higher than 72.29% of the traditional CNN model. Through the analysis of false detection rate, missed detection rate and average detection time, the detection performance of this algorithm was slightly better than Faster RCNN and Yolo algorithm. The average success rate of pig tracking based on this detection algorithm was 89.17%, and the average error of center point was 6.94 pixels, which showed the effectiveness and stability of the detection algorithm in pig tracking. Using this detection algorithm, it can lay a foundation for the future research on extracting the motion parameters of pigs to judge the health status of pigs.

Abstract:Aiming at the problems of manpower behavior spend and low monitoring accuracy of dairy cows, a cow behavior classification method was proposed based on that firefly algorithm to optimize support vector machine parameters by taking advantage of the data which obtained by wireless transmission neck ring. The method optimized the parameters of the support vector machine by using the firefly optimization algorithm to achieve the optimal classification accuracy. The experimental results showed that the wireless transmission collars can collect and transmit the cow neck activity information simultaneously. And the algorithm could effectively distinguish the three behaviors of different cows’ feeding, ruminating and drinking. The applicability was greatly improved. Among them, the optimal precision, sensitivity and accuracy rate were 97.28%, 97.03% and 98.02%, respectively. Compared with the conventional support vector machine algorithm, using the method proposed, the classification accuracy, sensitivity and accuracy of the same cow were increased by 13.39, 28.2 and 18.8 percentage points, respectively; the classification accuracy, sensitivity and accuracy of different dairy cows were increased by 0.74, 2.24 and 2.12 percentage points, respectively. The research results can provide technical support for further research on abnormal behavior detection and intelligent early warning of diseases in dairy cows.

Abstract:Aiming to achieve synergistic perception and joint data collection of pig body temperature and drinking behavior. In view of the low efficiency of traditional body temperature measurement, the inaccurate monitoring of drinking water, and the poor data availability, the implanted RFID temperature chip was applied to the measurement of pig body temperature in centralized pen housing breeding, and the water flow sensor was used for monitoring drinking water behavior. The combination of the two can achieve synergistic perception and joint data collection of pig identity ID, body temperature and drinking behavior. According to the structure of the automatic drinking water bowl for pigs and the scene when drinking water, a wireless monitoring node integrating water collecting flow sensor, RFID reader and ZigBee module was designed, and a monitoring system for automatically measuring pigs body temperature while drinking water was developed. Different experiments were designed for the monitoring of chip implantation depth, body temperature change and drinking water behavior. The experiments results showed that the system can measure the body temperature of different depths of pigs, and automatically monitor different drinking time and drinking water. The automatic association and simultaneous collection of data provided technical support for early warning and diagnosis of pig disease. The system effectively avoided manual operation errors, improved work efficiency, and can meet the fine management requirements of centralized pig farms.

Abstract:The weight of dairy cows is an important data in the process of healthy breeding. Aiming at the current problems of dynamic weighing, a dynamic weighing algorithm was proposed based on empirical mode decomposition (EMD). Firstly, the nonlinear and nonstationary oscillation signals collected by the weighing equipment were preprocessed to obtain the effective part of the signal. Secondly, the effective part was initially judged, and if it met the preset condition, it was the walkingstop state, the arithmetic average method was used to obtain weighing value. Finally, if it did not accord with the walkingstop state, the EMD algorithm can be used in distinguishing the slow walking, fast walking and strenuous moving states of the animal, and calculating the dynamic weighing value. In the algorithm design, it was found that the data acquired under severe motion fluctuated greatly and the weight value needed to be calculated after filtering. The experimental results showed that the dynamic weighing algorithm proposed can judge the motion state of dairy cows. The calculated weight value was less than 0.16% in the walkingstop state compared with the static weight. The error rate was less than 1% in slow walking and fast walking. The error in the state of motion was within 1.35% under strenuous motion. The research method can provide technical support for dairy cow dynamic weighing technology.

Abstract:Throughout the history of agricultural development, crop pests and diseases have always been one of the main obstacles hindering the development of agricultural economy. The crop disease identification system based on digital image processing technology had the characteristics of fast, accurate and realtime, which can help the farmers to take effective prevention measures in time. As an important technical means in the field of image recognition, deep learning has broad application prospects. The research progress of crop pest and disease image recognition technology in deep learning field in China and abroad was reviewed. The significance and necessity of deep learning technology research were clarified. The training samples of deep learning technology in image recognition research were large and the model structure was complex. Complex image recognition accuracy was low. It was proposed that improving the recognition accuracy of complex images would be the development direction of future research.

Abstract:Due to the randomness, volatility and intermittent characteristics of distributed generation, more and more distributed generation are connected to rural distribution networks, which makes the voltage reactive power control in rural distribution network more difficult. For the problem of voltage reactive power optimization in active distribution network, considering the reactive power compensation effect of distributed generation to distribution network, a voltage reactive power optimization control method suitable for rural distribution network which had distributed generation was proposed. By regulating the reactive power output of the distributed generation, adjusting the tap position of the onload tap changer, and switching the reactive power compensation equipment, the safe and reliable economic operation capability of the rural active distribution network can be effectively improved. The multiobjective coordinated optimization control model considering the distributed generation utilization rate, network loss, voltage fluctuation and reactive power equipment switching cost was established. A wellconverged cooperative particle swarm optimization algorithm was used to solve the multiobjective optimization problem. Finally, the IEEE 13 node power distribution system was used for simulation calculation, and compared with several multiobjective optimization algorithms, and the feasibility and effectiveness of the proposed model and algorithm were verified.

Abstract:Environmental perception is a vital part of driverless driving. The present widely used radar is trapped with its expensive cost and unitary information. Based on the deep learning technology, a joint training network referred to as spatial semantic network (SSN) was proposed, which can realize image segmentation and stereo estimation simultaneously. Through spatial mapping, SSN can input binocular images and output semantic point clouds. The SSN was trained by the KITTI dataset, and then the trained model was validated by KITTI test set, of which the verification result showed that the accuracy of image segmentation can reach 82.5%. And for the near points, the accuracy of stereo estimation can reach 95.5%, where the error within 5% was considered as accurate. Moreover, the processing speed can reach 0.135s per frame, generating around 48000 semantic cloud point coordinates per frame, which was close to the realtime requirement under lowspeed conditions, and had strong practical application value. 

Abstract:Target tracking is an important part of computer vision, specially the pedestrian detection and tracking is a crucial and difficult field. Many researchers have been devoted to the improvement of target detection and tracking methods. With the wide application of deep convolution network, the result of pedestrian detection and tracking has been improved. However, some complex scenarios are difficult to identify and track by present methods. Therefore, it’s necessary to propose an optimal algorithm to improve the performance of pedestrian detection and tracking. The region proposal network, which included multilayer competitive fusion model was used as pretraining network, and longterm and shortterm update strategy in pedestrian tracking task. The pretraining network applied VGG16 to extract feature maps, and then they were put into the multilayer competitive fusion region proposal network to generate more accurate candidate targets. The online pedestrian tracking algorithm was initialized by the pretraining region proposal network, and the region proposal network was finetuned through 500 positive samples and 5000 false positive examples from the first frame, and then created frame index datasets for longterm and shortterm update. Finally, the pedestrian tracking algorithm with continuous updating of region proposal network was accomplished. The model was verified by experiment and in the public datasets named by Caltech, ETH, PETS 2009 and Venice. The test result showed that the region proposal network which included multilayer competitive fusion model had the perfect performance in pedestrian detection and tracking task, and showed good effects in complex background environment.

Abstract:The suitable light environment could promote the photosynthetic rate of plants, increase the dry matter quality and then increase the fruit yield. In order to meet the adaptive regulation of greenhouse tomato light environment, a greenhouse tomato selfadaptive dimming system based on random forest-glowworm swarm optimization algorithm (RF-GSO) model was designed to realize the realtime collection of temperature, CO2 concentration and light intensity in greenhouse. At the same time, the information was transmitted to the software platform of greenhouse tomato selfadaptive dimming system through wireless sensor network. The platform could dynamically display the realtime ring. The ambient parameters could also realize the remote control of supplementary light. The ideal illumination intensity oftomato in greenhouse was calculated dynamically by RF-GSO algorithm, and the difference between the ideal illumination intensity and the measured illumination intensity of the sensor was taken as the control parameter to realize the adaptive control of the light environment of tomato in greenhouse. The experimental results showed that the determination coefficient R2 between the illumination intensity detected by the system and the target value of greenhouse dimming was 0.955, the root mean square error was 2.168μmol/(m2·s), and the system packet loss rate was 0.417%. It was showed that the adaptive dimming system of greenhouse tomato based on RF-GSO could achieve stable and reliable operation.

Abstract:In order to realize realtime feedback control for plant supplementary lighting based on the plant growth status, the LED lighting control system based on the FluorMonitor chlorophyll fluorescence sensor was designed. By the SDI-12 and MODBUS communication protocols, the parameters of chlorophyll fluorescence, light intensity and temperature acquisition were realized. According to the measurement process and acquisition cycle of chlorophyll fluorescence parameters of Ft, Fm and Yield, the automatic acquisition mode and manual acquisition mode were designed. Using the pulsewidth modulation (PWM) control mode, the LED light intensity control model was designed to realize the plant lighting accuracy control, and the display of light, temperature and chlorophyll fluorescence parameters and humancomputer interaction were realized through the design of manmachine interface. The supplementary lighting control experiment for lettuce was carried out. The results showed that the LED lighting control system based on chlorophyll fluorescence sensor can realize the stable control of light intensity, ETR and Yield. The system can improve the light utilization efficiency of lettuce, and the lighting realtime feedback control based on plant growth status was realized.

Abstract:Ammonia is one of the key environmental parameters affecting the healthy growth of pigs. And it is the key to ensure the healthy growth of pigs by timely and accurately grasping the trend of ammonia concentration in piggeries. In order to improve the accuracy and efficiency of ammonia concentration prediction in piggeries, a prediction model of ammonia concentration in piggeries based on empirical mode decomposition and long shortterm memory neural network (EMD-LSTM) was proposed. Firstly, the sequence data of ammonia concentration was decomposed to obtain the intrinsic mode function (IMF) at different time scales. Then, the longterm memory neural network prediction model was established for the intrinsic mode function. Finally, the prediction results of the components were summed as the final value of the concentration. The prediction model proposed was applied to the prediction of ammonia concentration in a pig farm in Yixing, Jiangsu Province. In order to verify the performance of the prediction model, the prediction model was compared with Elman prediction model, recurrent neural network (RNN) prediction model, longterm memory neural network prediction model and empirical mode decomposition and recurrent neural network prediction model. The results showed that the prediction accuracy of the empirical mode decomposition and longterm memory neural network model was higher. Compared with the real values, the mean absolute error, mean absolute percentage error and root mean square error were 0.0723mg/m3,0.6257% and 0.0945mg/m, respectively.

Abstract:According to the requirement of environment control of rabbit house with nonlinear and large lag, a regulation model of rabbit house environment was presented based on fuzzy reasoning, which integrated various environmental parameters of rabbit house. The error of environmental parameters, which included the temperature, humidity, carbon dioxide concentration and ammonia concentration, rabbit growth stage and season were taken as input of inference engine, and the Gauss type membership function was used to fuzzify the environmental parameters; totally 12 kinds of fuzzy control rules were set according to spring, autumn, summer and winter separately. The wet curtainfan control system and heat recovery system would be controlled by the fuzzy control rules to achieve the precise control of rabbit house environment. In order to verify the validity of the environmental regulation model of rabbit house, experiments were conducted in a rabbit house in Shandong Province from May to September 2018, and December 2018 to March 2019. The experimental results showed that the environment parameters of rabbit house can be controlled within the suitable range of rabbit growth and development by using the regulation model of rabbit house environment based on fuzzy reasoning, so as to ensure the growth and development of rabbits.

Abstract:Fresh agricultural products are the necessities of people‘s life. Reliable and efficient supply chain operation and management are of great significance to guarantee the quality of fresh agricultural products, and neural network technology has been widely used in many aspects of supply chain management of fresh agricultural products with its unique advantages. Based on the recognition of neural network technology’s advantages in the fresh agricultural products’ supply chain management, the current research about neural network technology application in the field of fresh agricultural products supply chain management was systematically reviewed. It was found that neural network was mainly applied to the risk evaluation and prediction, performance evaluation, quality monitoring and control, shelf life prediction and supply chain traceability, etc. Furthermore, aiming at the demand for the future development of neural network and supply chain management, the research trend in this domain was proposed. Firstly, the level of green and sustainable development would be posed more importance in supply chain management of fresh agricultural products. Secondly, neural network would be developed in the direction of neural network optimization, combined network model and deep learning.

Abstract:Fat content is an important indicator of the quality of walnuts. In order to achieve the rapid nondestructive detection of walnut fat content, the near infrared spectrum of walnut kernel was collected in the spectral range of 1040~2560nm. Multivariate scatter correction and standard normalized variate were used to preprocessing the original spectral information. And abnormal samples were eliminated by the Mahalanobis distance method. Then the feature bands were screened by the method, which combined competitive adaptive reweighting sampling (CARS) and correlation coefficient method (CCM) algorithm. Finally, the partial least squares regression and the support vector machine regression algorithm were used to establish prediction model for the fat content of walnut kernels. The results showed that with the six feature bands selected as input, the validation set coefficient of the walnut kernel fat content prediction model established by partial least squares regression algorithm was 0.86, and the root mean square error was 1.5849%. The validation set coefficient of model established by the support vector machine regression algorithm was 0.88 and the root mean square error was 1.3716%. It was showed that the modeling quality of the support vector machine regression algorithm was better than the partial least squares regression algorithm. The support vector machine regression prediction model established by the feature bands could sharply reduce the modeling complexity and realize the rapid nondestructive detection of the fat content of walnut kernel.

Abstract:Nowadays, food safety issues have been concerned by both governments and consumers. However, the increasing number of food safety related articles makes it difficult to retrieve useful information from the articles in a short time with high accuracy. In order to improve the efficiency and accuracy of accessing food safety information, a question answering system was proposed, which was based on long shortterm memory (LSTM) and information retrieval techniques. The system relied on the food safety unstructured texts obtained by Web crawler technologies, and question answer pairs were selected by using Lucene, and LSTM was used to predict answers according to matching degrees between question and candidate sentences. Based on Lucene’s retrieval mechanism and the LSTM model, a question answering system was built which can select sentences that were most likely to contain the answer to given questions. The results showed that the proposed system outperformed the baseline which was only based on retrieval mechanism. Moreover, the performance analysis were made for the two methods with respect to the numbers of candidate articles and candidate sentences.

Abstract:The mechanism for information exchanging has not yet been established between the agricultural product production monitoring and management system and agricultural product safety traceability system. In China, almost every agricultural product traceability system develops its traceability coding scheme according to its specific system thus only adapts to its own system other than general using purpose, and it has led to so called information “islands” phenomenon along the agricultural product supply chain. In order to carry out agricultural product traceability from its planting to marketing, the basic structural characteristics of each key node in the agricultural product supply chain was analyzed, taking full advantages of the existing coding system, a traceable encoding approach was designed based on the existing coding system of global standard one (GS1), that was, International Article Coding Association and the United States Uniform Code Committee (EAN·UCC).The proposed traceability coding scheme for agricultural products was suitable for agricultural product supply chain related enterprises and individual operators because it addressed the identification problems for individuals and small or mediumsized enterprises which had not obtained the manufacturer’s unique registration code from GS1. This traceable coding scheme was designed for different circulation subjects, and it can be used to measure up to the flexible scalability by mutual conversion between GS1 coding and electronic product coding (EPC).This coding scheme was settled for the requirements of the traceability system for information transmission and information traceability, and supported the customized queries of the traceability information, which can meet the requirements of the traceability information interaction between enterprises, thereby improving the operation efficiency and functionality of the agricultural product quality and safety traceability system. Experiments showed that the designed agricultural product coding system performed great flexibility as well as scalability and by providing different agricultural product quality and safety traceability systems with general information medium, it could effectively facilitate the establishment of information communication among companies, industries or sectors who were engaging agricultural planting, processing, distributing and selling.

Abstract:The consumption on the freshcut fruit is growing significantly, in order to satisfy the requirement of the consumption with high quality and accuracy quantity, a freshness detection device for freshcut fruit was developed using visible/nearinfrared spectroscopy. The device was designed based on spectral analysis and sensor technology, which was integrated electronic components, including micro spectrometer, gravity sensor, illuminance meter and raspberry pie display. It was operated with hardware and software system, in which the hardware system included a data acquisition module, a light source module, a result output displays module, and a controller module. The software implements functions such as processing data, invoking a hierarchical model, and feedback grading results. Taking freshcut apples as an example, the spectral reflectance of 400~820nm bands was collected from 24 red Fuji apple samples, which were in the range of 0~2h, 2.5~8h and 8.5~30h, respectively. The samples were measured for four sets of spectral data, and a total of 288 raw sample data were obtained. The apples were divided into two grades in a cutoff time of 2h. After processing the reflected spectral data onto 15 points of S-G smooth convolution, the kernel function was used as the support vector machine of Gaussian kernel function (RBF) to establish the apple freshness visibility/near infrared spectrum detection hierarchical model. The accuracy of prediction set was 86.81%. The freshness detection device for freshcut fruit using visible/nearinfrared spectroscopy could provide a technical support for the freshness identification nondestructively and rapidly during the storage after cutting.

Abstract:The electronic tongue system based on virtual instrument technology was used to qualitatively analyze the aged wheat with four storage years to achieve rapid and objective evaluation and analysis of aged wheat with different storage years. In view of the complex output signal of the electronic tongue and the large amount of data, the wavelet packet transform was used to extract the eigenvalues of the original data to reduce the data dimension and reduce the data size. On this basis, the improved fish swarm algorithm was used to optimize the parameters of the extreme learning machine, and the analysis model of wheat storage age was established. The model was used to qualitatively analyze the aged wheat with five storage years. The experimental results showed that the model had better classification effect and the classification of WPT-IAF-ELM was compared with genetic algorithm and particle swarm optimization ELM algorithm respectively. The effect was better, and the training set correct rate, test set correct rate, overall classification accuracy and Kappa coefficient were respectively 96%, 92%, 95% and 0.91, which indicated that the proposed combined model had better classification effect for food.