Evaluating the Seedling Emergence Quality of Peanut Seedlings via UAV Imagery

Accurate evaluation of peanut seedling emergence is critical for ensuring agronomic research accuracy and planting benefit efficiency, but traditional manual methods are limited by strong subjectivity and inconsistent batch inspection standards. In order to quickly and accurately evaluate the emergence rate and quality of peanuts, this study proposes an intelligent evaluation system for peanut seedling conditions, which is constructed based on an improved YOLOv11 combined with the Segment Anything Model (SAM) for peanut seedling emergence evaluation, using high-resolution images collected by Unmanned Aerial Vehicles as the data foundation. Experimental results show that the improved YOLOv11 model achieves a detection precision of 96.36%, a recall rate of 96.76%, and an mAP@0.5 of 99.03%. The segmentation performance of SAM is outstanding in terms of integrity. In practical applications, the detection time for a single image by the system is as low as 83.4 ms, and the efficiency of video counting is 6–10 times higher than that of manual counting. Without extensive data annotation, this method performs excellently in peanut seedling emergence quantity statistics and growth status classification, providing efficient, accurate technical support for refined peanut cultivation management and mechanical sowing quality evaluation.