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Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery's autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery. [ABSTRACT FROM AUTHOR]

The research and application of agricultural robots is an important trend in the development of smart agriculture, and the target application robot is an important branch in the field of agricultural robots. This study summarizes the current research status and progress of domestic and international targeted spraying robots, and introduces the working principle and main technical points of targeted spraying robots. The study analyzes the current research status of key technologies for targeted spraying robots, including plant diseases and insect pests detection technology, targeted spraying technology, autonomous walking and control technology, and elaborates on the research progress and challenges of these key technologies both at home and abroad. It is pointed out that currently, most targeted spraying robots domestically and internationally can achieve the effects of saving pesticides and improving pesticide utilization rate, but their development is mostly in the laboratory stage and cannot fully meet actual production tasks. This study can provide reference and ideas for the advancement of targeted spraying robot research and the development of intelligent agriculture. [ABSTRACT FROM AUTHOR]

Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery’s autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery.