The identification of straight-curved rice seedling rows for automatic row avoidance and weeding system.

Mechanical weeding can solve the problems of environmental pollution caused by chemical weeding and high labour intensity of manual weeding. However, the traditional mechanical weeding method will result in the seedling damage due to the bending of the seedling rows. In this paper, an identification method of straight-curved rice seedling rows based on initial clustering at the bottom, and exterior point elimination at the top, is proposed. The approach proposed can effectively solve the influences of several duckweeds, the random weed distribution, changes in light intensity, and the curvature changes of the seedling rows. The identification of straight-curved seedling rows mainly includes the dynamic collection of rice seedling images, the detection of rice seedlings based on the improved CS-YOLOv5 model, and the extraction of seedling strip lines based on initial clustering and exterior point elimination at the top. The field experiments indicated that the average identification angle deviation was less than 1°. • Modifiy a deep learning model to detect rice seedlings under different conditions. • Extract the seedling strip lines of straight-curved rice seedling rows. • Seeding row identification has an average angle deviation of less than 1°. [ABSTRACT FROM AUTHOR]