Quick and accurate monitoring peanut seedlings emergence rate through UAV video and deep learning

During the seedling stage, real-time monitoring and detection of seed germination are important for testing the quality of seeds, crop field management, and yield estimation. However, owing to the low efficiency of traditional manual seedling rate monitoring, survey methods have been gradually replaced by unmanned aerial vehicles (UAVs) and real-time peanut video counting models. In this study, we propose an efficient and fast real-time peanut video counting model (combining the improved YOLOV5s, DeepSort, and OpenCV programs) to accurately distinguish peanut seedlings from weeds, and to count peanut seedlings based on videos. The improved YOLOV5s combines a vision transformer with CSNet to replace the original CSNet backbone. The field experiment results show that the real-time peanut video counting model count capabilities is close to those of humans with an accuracy of 98.08%; however, the seedling calculation model takes only one-fifth of the time required for human detection. Therefore, the video-based model outperforms the image-based target detection algorithm, and was more suitable for application in practical germination rate investigation in peanut production.