Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

Abstract An agronomic trait such as stand count is important for cultivar development and crop management practices. Manually counting the number of plants is time consuming, labor‐intensive, and prone to error. The use of unoccupied aerial systems (UAS)‐collected red, green, blue (RGB) imagery in conjunction with advanced deep learning and image processing methods might serve as an accurate, reliable, and affordable alternative to manual stand counting. This paper presents a thresholding framework for high‐accuracy stand counting in dry beans and comparing it with the performance of the newest YOLO12 detection and segmentation models on a time series UAS RGB dataset and converting the findings into an easy‐to‐use web application. The methodology involved collecting multiyear RGB data, developing a custom ArcGIS Python library pipeline for YOLO (you only look once)‐compatible dataset unification, and comparing three distinct counting approaches. Results demonstrated the clear superiority of the deep learning segmentation architecture. While the thresholding method provided a reliable baseline ( R 2 = 0.922), the YOLOv12 segmentation model achieved a robust R 2 of 0.972 with a significantly lower mean absolute error of 3.2 and an mAP50 of 0.941 (where mAP is mean average precision). In contrast, the YOLOv12 segmentation model, leveraging pixel‐level supervision and a multi‐class structure to resolve overlapping plant clusters, achieved a robust coefficient of determination ( R 2 = 0.972) with minimal bias. This high performance, attained using only affordable RGB imagery, validates the use of low‐cost sensors for precision phenotyping. The framework was contained in a Flask web application that provides nontechnical users direct access to the high‐accuracy model and generates georeferenced outputs, effectively bridging the gap between research and practical decision‐support tools for dry bean breeders and growers.