A corn seed spacing detection method based on image stitching and YOLOX

Corn seed spacing is fundamental for detecting doubles and misses and calculating the seed spacing conformity and variation. These factors are essential for optimizing the corn seeding operation system and improving the quality of corn seeding. This paper proposed a corn seed spacing detection method based on image stitching and YOLOX in the seedbed. The image stitching algorithm based on the "speed-rate" linkage could generate a seedbed panorama by stitching the images obtained from an industrial camera. As a result, the seedbed panorama could present the overall distribution of seeds in the detection region. The YOLOX-based seed spacing detection algorithm was utilized to detect the seeds in the seedbed panorama, determine the position coordinates of each seed, as well as compute the seed spacing. This paper established a corn seed spacing detection system based on the above method and constructed the soil trough and field experiments. The soil trough experimental results indicated that: (1) The seedbed panorama generated by the image stitching algorithm based on the "speed-rate" linkage could accurately reflect the seedbed information; compared to other image stitching algorithms based on feature extraction, the image stitching algorithm based on the "speed-rate" linkage had obvious advantages in stitching speed and completeness of seed spacing information. (2) With a score threshold of 0.5, YOLOX had an accuracy of 96.67 %, a recall of 97.89 %, and an F1 of 97 %, which could be adapted for seed detection in seedbed images. (3) The missed detection rate of the YOLOX-based seed spacing detection algorithm in the seedbed panorama was 3.8 %. Compared to the seed spacing of the seedbed panorama was measured employing ImageJ (IM), the average error of the YOLOX-based seed spacing detection algorithm was 1.99 %. Comparing the seed spacing of the seedbed panorama obtained by the YOLOX-SSDA (YD) with IM, the average distinction between the two detection results was 1.53 mm, and the average detection error for seed spacing was 2.79 %. The field experimental results indicated that at different forward speeds (6 km/h, 8 km/h, and 10 km/h), corn seed spacing detection accuracy was 95.5 %, 93.2 %, and 90.6 %, respectively. Furthermore, the feasibility and accuracy of the corn seed spacing detection method in the seedbed were demonstrated. This research positively impacted the advancement of seeding detection technology and the enhancement of corn seeding quality.