Design of crop pest detection robot based on ROS system

Xiwen Wei; Yanhong Liu; Cong Tian; Gang Liu; Liang Zhu; Gaojie Nian

doi:10.1117/12.3049617

8 November 2024 Design of crop pest detection robot based on ROS system

Xiwen Wei, Yanhong Liu, Cong Tian, Gang Liu, Liang Zhu, Gaojie Nian

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341634 (2024) https://doi.org/10.1117/12.3049617
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

With the continuous development of science and technology, the level of automation and intelligence in the agricultural field is also constantly improving. In order to improve agricultural production efficiency, reduce labor intensity and reduce resource waste, more and more agricultural robots have emerged. In this paper, an intelligent disease and pest detection robot based on ROS system is designed. The robot uses Yolov5s as the algorithm framework of crop disease and pest detection, and the fusion of SLAM and TEB algorithm as the algorithm framework of autonomous navigation, which improves the problems such as low precision of robot disease and pest detection and unstable path planning. The test results show that the robot can monitor farmland environmental parameters in real time, automatically identify pests and diseases, and have autonomous navigation and human-computer interaction. Providing relevant data and recommendations to assist farmers in pest control will help improve agricultural production efficiency and crop quality, reduce the use of pesticides, and promote sustainable agricultural development.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiwen Wei, Yanhong Liu, Cong Tian, Gang Liu, Liang Zhu, and Gaojie Nian "Design of crop pest detection robot based on ROS system", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341634 (8 November 2024); https://doi.org/10.1117/12.3049617

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