Development and Testing of Row-Controlled Weeding Intelligent Robot for Corn

Abstract

Corn row-controlled weeding is a critical crop field management aspect. Corn row-controlled weeding robots suffer from large errors in seedling and weed identification algorithms, lack of row weeding actuators in dry fields, and low integration of automated devices for identification, navigation, and weeding. Therefore, this paper investigates an intelligent robot for row-controlled weeding, which could realize the integrated automatic operation of seedling and weed identification, row line acquisition, automatic navigation, and row-controlled weeding. We present a fully integrated, autonomous, and innovative solution for row-controlled weeding robots to overcome the difficulties of weeding to row. The solution constructs a seedling and weed identification model based on the YOLOv5 model and an improved boundary loss function. It also investigates a real-time extraction method for corn seedling strips based on region-of-interest updates. In addition, we developed an intelligent control device that can realize row-controlled weeding and depth control, adjusting weed height, depth of entry, and weed spacing. Finally, a fully autonomous weeding robot system was developed and integrated. Field tests showed that the intelligent robot could continuously cruise autonomously for weeding, with a weeding rate higher than 79.8% and a seedling injury rate lower than 7.3%. These efforts have laid a solid foundation for the future commercialization of intelligent weeding robots.