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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]