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Agriculture is crucial for economic development. Losses in agricultural production were caused by a variety of circumstances, but pests and illnesses are among the most important. The use of plant protection machinery to spray pesticides is still the most effective method of pest control at present. Traditional agricultural sprayers were inefficient and time-consuming, which caused pesticide waste and environmental contamination. In the process of spraying pesticides, farmers are directly exposed to pesticides, which is easy to cause bodily harm or even poisoning. With the steady progress of China's agricultural modernization, robots are widely used in agricultural plant protection work. Intelligent pesticide spraying robots are an effective method to solve the problem of applying pesticides in complex environments such as hills, orchards, fields, and greenhouses, which is more efficient and accurate. Intelligent spraying robot not only saves labor but also reduces the improper use of resources and environmental pollution. It is conducive to our better reaching of the goal of agricultural sustainable development. In this study, the research progress analysis of an intelligent pesticide spraying robot focuses on four respects: mobile chassis design, spraying the device design, navigation technology, and target detection technology. This paper discusses the factors limiting the development of intelligent pesticide-spraying robots for the primary technologies applied to various operation scenarios. Firstly, the mobile chassis design of the spraying robot was analyzed from the ground spraying robot and aerial spraying robot. The study and development of wheel-track composite robot chassis can integrate the advantages of wheeled chassis as well as a tracked chassis, which will be the research trend of ground intelligent spraying robot. Plant protection UAV is unrestricted by terrain, and can be applied to a variety of scenarios, which will be the key orientations for future research. Secondly, the spray devices required by the spraying robot in different spraying scenarios were summarized. It mainly includes an adjustable boom sprayer device, electrostatic spraying device, targeted spraying device, air-assisted spraying equipment, and profile modeling spray equipment. The most commonly used navigation methods are analyzed, including Global Positioning System (GPS) navigation, visual navigation, inertial navigation, multi-sensor fusion navigation, laser radar navigation, odometer, geomagnetic navigation, etc. Additionally, the detection methods of various pesticide spraying robots are evaluated from the perspectives of crop detection and pest detection. The deep learning algorithm can automatically extract the image features of crops, make use of the information, and exactly identify the location of crop pests and diseases, to quickly respond to achieve accurate spraying. Finally, the development trend of intelligent pesticide application robots for precise application is presented. Six requirements or prospects were proposed for the intelligent pesticide spraying robot development in the future, including to improve the response speed of the spraying robot, to improve detection accuracy, to design lightweight chassis, to reduce the development cost of the spraying robot, and to integrated air-ground spraying technology. In conclusion, intelligent and precise application robots have the potential to significantly improve the quality of precision agriculture operations by facilitating intelligent decision-making on operational parameters before actual operation. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAV) have widely been served as a new technology in agricultural aviation plant protection and pest control in China. However, it is highly demanded to improve the accuracy of UAV spraying pesticides and operational efficiency. In this study, a Variable Rate Application (VRA) of a centrifugal spraying system was designed for precise placement and timing of pesticide application in specific field conditions using the network Real-Time Kinematic (RTK) technology. STM32F103 single-chip microcomputer was used for the core controller. A serial port of controller was selected to obtain the global position system (GPS) information. A network modular Data Terminal Unit (DTU) was connected to realize the network RTK technology via the serial port of the controller. The control voltage was used to tailor the speeds of the centrifugal nozzle motor and peristaltic pump. A Pulse Width Modulation (PWM) was used to control the output voltage from the core controller to the armature of motors in this system. The speed of the centrifugal nozzle motor determined the output droplet size, while the speed of peristaltic pump determined the flow rate and amount of spray fog. The agricultural dataset before the system working was acquired using the airborne multispectral camera, ground object spectrometer, and handheld GPS. A prescription chart was also constructed using ArcGIS software. In the system working, the GPS modular was used to capture the location longitude and latitude data for the system to read and analyze. The system was always real-time searching the UAV geographical position during the spraying pesticides process using the GPS data, and then matching the position using the orthogonal grid. Meanwhile, the system was used to real-time tailor the changes in the duty cycle of output PWM, after matching the decision of the prescription map. The speeds of the centrifugal nozzle motor and the peristaltic pump were then to control the particle size and application amount of the UAV. The operation data was finally uploaded to the monitoring platform for real-time display and storage. Several spraying experiments were carried out in the research base of South China Agricultural University in Zengcheng City, Guangdong Province of China. The airborne spraying device was carried on the MG-1p plant protection UAV using a developed DJI Drone. The sampling points of droplets were set to match the planting density of crops, where the water-sensitive paper was used to collect the droplet data. After UAV operation, the water sensitive paper was collected into a plastic bag for later use. An hp4678 scanner was selected to map the collected water-sensitive paper after the experiment. A DepositScan software was utilized to analyze the water-sensitive paper in each sampling area after image acquisition. A detailed dataset was obtained, including the deposition amount, coverage density, and particle size of droplets. The data demonstrated that the accuracy of the device was within 0.6 m at the operating speed of 2 m/s. The application effect was finally stabilized within 2.2 m in the case of continuous operation. Particle size varied smoothly in the boundary area of prescription, as the rotating speed of the centrifugal nozzle increased. The findings can provide a sound basis for the improvement of variable application technology of plant protection UAV in precise agriculture. [ABSTRACT FROM AUTHOR]

Agricultural machinery is one of the important components of modern agriculture. In recent years, the number of agricultural machinery has continually increased, as well as the fuel consumption caused by agricultural machinery. The fuel consumption of agricultural machinery is directly related to agricultural production costs and the vital interests of farmers. Estimating the fuel consumption of agricultural machinery is of great significance in environmental governance, agricultural machinery operator evaluation, and agricultural cost input. Different from road vehicles, the factors affecting agricultural machinery fuel consumption seem to be more complex. Taking driving conditions for the only consideration cannot accurately predict the fuel consumption of agricultural machineries. Random forest, as a typical representative of ensemble learning, has many applications in various fields and has strong fitting ability for nonlinear data. It is widely used in the research of vehicle fuel consumption prediction. The purpose of this article is to realize the fuel consumption prediction of the grain harvester, WORLD 4LB-150AA, during working in the farmland. Based on the engine operating condition data and driving condition data collected by the harvester CAN(Controller Area Network) bus and GPS (Global Positioning System) terminal, seven indicators are constructed, including engine mean torque, engine mean speed, average speed, mean acceleration, mean deceleration, acceleration standard deviation and deceleration standard deviation. The acquisition frequency of the average data is 1.3 s, and the total number of the records is 130 788. Agricultural machineries that provided the data worked in six provinces including Liaoning, Jilin Province, Shandong, Jiangsu, Zhejiang, and Hubei. At the same time, by calculating the Spearman correlation coefficient, the correlations between seven indicators and fuel consumption were explored. According to China's agricultural divisions, the six provinces are divided into three regions: northeast region, plain region, and hilly region. Then, the fuel consumptions of the same grain harvesters in different regions were analyzed. Above the analysis, the fuel consumption prediction model of the harvester based on Random Forest was established, and compared with the one based on support vector machine. The results showed that the fuel consumption is correlated with all indicators. Among them, the fuel consumption is highly correlated to engine mean torque, engine mean speed and average speed, all with the correlation coefficient above 0.6, followed by mean acceleration, mean deceleration, acceleration standard deviation and deceleration standard deviation, whose correlation coefficients are above 0.4. There are significant differences in the fuel consumption of harvesters working in different regions. Among them, areas with high output per unit area are also relatively high in fuel consumption. Moreover, the Random Forest based model can realize the higher accurate prediction of fuel consumption during harvester operation. The root mean square error RMSE is 0.14, the average absolute error MAE is 0.24, and the coefficient of determination R2 is 0.84. The proposed method in this paper can provide a reference for the optimization of working conditions of agricultural machinery and precise fuel consumption monitoring. [ABSTRACT FROM AUTHOR]

Currently, most harmless disposal refrigerated vehicles use the global positioning system (GPS) to determine the position of disinfection stations and the transport routes of pigs died of diseases. However, the single navigation and positioning information might be unstable when the vehicle was running in remote mountainous area, the information transfer was not continuous, incomplete and untrue. In order to solve this issue, a monitoring system was designed based on radio frequency identification technology (RFID), Beidou navigation satellite system (BDS) and global positioning system (GPS). This paper makes full use of the BDS/GPS dual-mode receiver, which supports positioning and two-way communication functions. On the one hand, it can solve the problem of positioning information instability that occurs when using a single positioning. On the other hand, when the BDS navigation positioning system data appeared to show a deviation, The GPS positioning data from the WGS-84 coordinate system was converted to the CGCS2000 coordinate system of the BDS positioning system, to achieve the purpose of improving the acquisition accuracy of the transport vehicles location information. GAMIT software was used to calculate the 3D coordinates of the observation point, and it was analyzed for the positioning accuracy of combined navigation and single navigation. Floyd algorithm was combined to optimize transportation routes to search for the shortest transportation distance. The harmless transportation vehicles walking map could be drawn from the positioning information of the BDS/GPS dual-mode receiver. The message communication function of BDS satellite was used instead of GSM short message service to realize remote transmission between harmless transportation vehicles and control center. When pigs died of diseases are transported to fixed-point disinfection station, RFID reader will read the vehicle-mounted RFID active electronic tags to recognize the transport vehicles and the GPRS network will transmit the fixed-point disinfection monitoring data to the monitoring center. Monitoring center uses the ArcGIS map display to carry out the real-time monitoring of vehicle transport routes, and uses text to speech to realize the intelligent deployment and management of transport vehicles. Meanwhile, monitoring center obtains the transport routes of pigs died of diseases and the fixed-point disinfection monitoring information by analyzing the received data. After field test, the positioning accuracy of BDS/GPS dual mode receiver was 55.13% higher than that of BDS satellite positioning, and 52.71% higher than that of GPS satellite positioning. The networks packet losses value of vehicle terminal acquisition system was 0.26%, and the error rate of vehicle identification of the disinfection point was 0.97%. The results showed that the monitoring system met the requirements of the monitoring and management system in mountainous areas, and it ran reliable and stable. The study provides an effective method for a transport vehicle for the harmless treatment of dead pigs, accomplishes the comprehensive supervision and management of transport vehicles for the harmless treatment of dead pigs. This system can also be adopted as a reference to harmless disposal systems for other pigs that die of diseases, it has practical value. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) and inertial navigation system (INS) are widely used in target positioning, vehicle navigation, precision agriculture and other fields. However, due to factors such as satellite signal occlusion, multi-path effect and observation error, the filtering results usually have large errors. Kalman filtering algorithm is generally used in navigation and positioning system to improve positioning accuracy. The performance of kalman filter algorithm depends on the dynamic model of state vector and the random model describing noise characteristics. There are 2 corresponding adaptive kalman filtering algorithms: one is the multiple-model-based adaptive estimation (MMAE); the other is the innovation-based adaptive estimation (IAE). The first method is to combine all models with non-zero model probability by using a set of parallel kalman filters under different dynamic models and statistical information. The second method complete the adaptive filtering directly by calculating the observation noise or process noise covariance matrix based on the change of information sequence. In MMAE and IAE methods, discrete time differential models are usually adopted, such as the constant velocity CV model and constant acceleration CA model, to describe the change process of state variables. However, the state vectors such as position, velocity and attitude are correlated, and it is very difficult to accurately describe the statistical relations of these states. When the prior information is not sufficient, the coupling effect of each filtering state will also cause large errors to the positioning results. Another disadvantage of kalman filter algorithm based on discrete time differential model is that it highly depends on the prior statistical information of process noise and observation noise. Generally, the prior statistical information of process noise and measurement noise depends on the motion process and application scene, which is difficult to be obtained accurately. Insufficient prior statistical information of the filter will reduce the estimation accuracy of the filter state and even lead to the divergence of the filter estimation results. The research of adaptive kalman filter algorithm is mainly focused on the covariance of online calculation process noise or measured noise. In order to improve the accuracy of navigation and positioning, an adaptive kalman filter algorithm based on FIR (finite impulse response) prediction model for white noise background was proposed in this paper. Firstly, the continuous trajectory function of moving target was approximated by an N-order polynomial with arbitrary precision, and the FIR prediction model polynomial was obtained. The FIR prediction model coefficient was obtained by solving a convex quadratic programming problem, and the optimal solution of FIR prediction model coefficient was solved by lagrange multiplier method. The convex quadratic programming taken the polynomial motion law of the target as the constraint condition and the minimum white noise gain as the objective function, and the optimal solution could not only satisfy the constraints of the target's motion state, but also had the effect of de-noising to a certain extent. Finally, the proposed FIR prediction model was combined with kalman filter. Simulation test and the measurement results showed that kalman filtering algorithm based on FIR prediction model had higher estimation accuracy than the traditional kalman filtering algorithm under the same parameter settings, and the simulation experiment results showed that the localization precision was increased by 29.54%, the measured experimental results showed that positioning accuracy in east-west direction increased by 21.71%, positioning error in north-south direction increased by 22.62%. The proposed algorithm could be used for single state estimation before information fusion in loosely coupled GPS/INS, and also for noise reduction in post-processing of GPS receivers. [ABSTRACT FROM AUTHOR]

The rapid development of modern agriculture in China has put forward higher requirements for agricultural machinery operation. In terms of area measurement, GPS (global positioning system) has become an important measuring tool, completely changed the traditional mode of operation, liberated the labor force, and improved the operation efficiency. Field operation is still basically in the stage of manual operation, so it is inevitable that there is much repetitive operation and missing operation. How to accurately measure the area of operation, this is a necessary issue. In this paper, the adaptive Kalman filter was used to improve GPS positioning accuracy for accurately measuring the tractor operation area. The adaptive Kalman filtering algorithm was mainly to solve the problem of the degradation of the system’s filtering accuracy and the divergence of the system in the case of noise statistics being unknown or not accurate. In order to achieve the system noise estimation of adaptive filtering, we used the covariance matching technology and the Kalman filter residual error to realize the algorithm. In this research, the LABVIEW software was used to get latitude and longitude data of GPS receiver. And then the Gauss projection algorithm was used to change the longitude and latitude data into plane coordinates to calculate the area. To test and verify the influence of different ways of operation on the operation efficiency, back tillage, spindle tillage and alternative plough method were chosen. Firstly, this research used MATLAB to identify the operation trajectory, then used different color to show the area of operation, and used the image processing method to calculate the effective operation area, the actual operation area, and the missed and repeated tillage rate, which were used to evaluate the operation efficiency of the tractor. In order to verify the feasibility of the algorithm, the accuracy of single point positioning and the accuracy of area measurement were tested. The single point positioning experiment showed that the Kalman filter improved the accuracy of GPS single point positioning. The numerical changes at the x and y direction before filtering had relatively big fluctuation, and became flat after filtering. The mean value of coordinates changed after filtering, and the mean square error became smaller. The x coordinate reduced from 0.06317 to 0.05807 m after filtering, and the y coordinate reduced from 0.07901 to 0.04097 m after filtering. In the test of GPS area measuring precision, which was the preparation for its work in the measurement of field area, this research used the GPS to measure some regular and irregular figures. The result showed that the relative error of area measurement was 2.09%. Finally, the field experiment was conducted. The result showed that Block 1 missed tillage rate was 14.29%, and repeated tillage rate was 6.19%; Block 2 balk rate was 10.72%, and backset rate was 5.54%; Block 3 balk rate was 1.81%, and backset rate was 6.82%. With the measurement area increasing, the measurement accuracy was higher. The most efficient farming method was alternative tillage, and the second was spindle tillage. Back tillage’s balk rate was the highest, and its operating efficiency was the lowest. Image processing method was used to calculate the backset and balk acreage in this paper. Different colors were used to display normal area, repeated tillage area and missed tillage area, through which it could visually display missed and repeated tillage locations, and then calculate the working efficiency. We can use this method to guide the actual agricultural production operation, and select the operation mode with high efficiency. [ABSTRACT FROM AUTHOR]

Aquaculture involves cultivating freshwater and saltwater populations under controlled conditions, in which the high water quality plays an important role for the harvest of aquatic organisms. This paper proposes a water quality monitoring system to achieve that goal. While current water quality monitoring devices share drawbacks of small measuring range, poor mobility and high cost, the distinguished contribution of water monitoring is a self-learning navigation component, which can address the previously mentioned challenges in other systems. Our system contains a front-end water monitoring subsystem, as well as a back-end server to store and analyze the monitored data. We developed three main modules in the front-end monitoring subsystem: a water quality collection module, a vessel movement control module, and a GPS navigation module. The water quality collection module contains a PT100 temperature sensor, a fluorescence dissolved oxygen sensor, and an industrial pH meter. Those sensors are used to collect parameters related to water quality including water temperature, dissolved oxygen, and the pH value. The vessel motion control is remotely managed by a CC2530 chip, which periodically sends commands to the motion coordinator in the ship. All data from the monitoring subsystem, including the water quality parameters, vessel movement control commands, and the GPS locations, are sent to the GPRS layer, which acts as a bridge to connect the monitoring subsystem and the server. Once the server received data, it parses them and calculates the water temperature, the dissolved oxygen and PH values. Meanwhile, the server extracts the location information and computes the distance and the direction angle to the target position. We have designed a database to store the collected data in the server, and also developed an Android application so that individual users can access the data at all time and places. The user can even set measurement target and control the movement of the vessel directly by the Android client. This process is achieved by following steps: 1) the Android client sends control commands to the server; 2) the server calculates the steering angle based on the current state of vessel and location information, and sends a corresponding control command to the GPRS module; 3) the GPRS module passes the message to CC2530 chip through the RS485 serial port; 4) the chip simulates PWM waves to control the left and right motor revolution so that vessel can change direction and move freely as expected. The vessel gradually revises its path according to the received data and its current GPS location, and will move towards the final target eventually. Our system has been evaluated in a modern fishery breeding base in Yangzhong, Jiangsu Province. In the experiment, the ship was initially driven by manual control to select twelve measurement positions. After that, we utilize our self-learning system to navigate the ship to access those target positions. The ship stayesat each location for two minutes and collectswater quality parameters in the neighborhood. After an hour of testing, the errors between navigated positions and real target positions areless than 2 meters on average, and the maximum difference of dissolved oxygen value between those positions is 1 mg/L. The change of water temperature is 1.5 °C, and pHvalue remains unchanged. Those results are consistent with the horizontal distribution law of water quality parameters. Compared with current state-of-arts, our system has the capability of mobile data collection, which can not only increase the measurement range but also reduce the cost. The system has significant potential in various applications such as aquaculture, river management, and hydrological monitoring. [ABSTRACT FROM AUTHOR]

At present, most combine harvesters are using Global Positioning System (GPS) to get the position of harvesters and draw harvesters' work trajectories. However, the positioning information might be instable by using a single positioning. In order to solve this problem, this paper makes full use of Beidou/GPS dual-mode receiver which supports positioning and two-way communication. The inside of the Beidou/GPS dual-mode receiver contains 2 positioning systems: Beidou Navigation Satellite System (BDS) and GPS. But these 2 positioning algorithms are using 2 different coordinate systems: Beidou navigation positioning algorithm uses BJ-54 coordinate system, and the positioning algorithm of GPS navigation uses WGS-84 coordinate system. Complementary use of these 2 positioning systems can solve the problem of positioning information instability that occurs when using a single positioning. It means that when the Beidou positioning information has deviation, GPS positioning information can be used to make correction. The combine harvester walking map can be drawn from the positioning information of the Beidou/GPS dual-mode receiver. The message communication function of Beidou satellite is used instead of GSM short message service to realize remote transmission between harvester and control center. The integrated management system for grain combine harvester is designed using sensor detection technology, data acquisition and processing technology, wireless communication technology, navigation technology, speech synthesis technology and computer network technology. The integrated management system of grain combine harvester includes 2 parts: Vehicle subsystems and control center. The vehicle subsystem consists of Beidou/GPS dual-mode receiver, weight sensor module, moisture sensor module, speed sensor module and text-to-speech (TTS) module. The control center consists of clients and server. The vehicle subsystem collects the data of geographical position, harvested area and grain weight, and then transmits the data to the control center by Beidou satellite. The server of control center analyzes and processes these data, and then draws the harvester's work trajectory map and the yield map, and the clients of control center can query the harvester's work trajectory map and the yield map of designated position. In order to achieve integrated management and scheduling for harvesters, the control center can send text commands to the harvesters, and the text commands can be converted into speech through TTS technology. In the field tests, the harvester runs with the speed of 3.6 km/h. The actual area of the harvesting is about 4 716 m², the actual total mass of grain is 3 086 kg, and the average yield is 6 544 kg/hm². However, the harvesting area is 4 853 m², the total mass of grain is 3 193 kg and the average yield is 6 579 kg/hm² measured by the system. The relative errors are 2.9%, 3.47% and 0.55% respectively. From the result we find that this integrated management system of grain combine harvester has a certain error, but the precisions of harvesting area and grain weight both reach 96%. The tests show the system is running stably and reliably. Therefore, this system is valuable for yield monitoring and trans-regional operation management of grain combine harvester in southern hilly area of China. [ABSTRACT FROM AUTHOR]