Deep learning and computer vision for image‐based high‐throughput phenotyping of canning quality traits in dry beans

Abstract Canning color retention is a key quality trait in dry bean ( Phaseolus vulgaris L.) breeding, influencing consumer acceptance and commercial value. Public breeding programs maintain canning quality as a selection trait of importance, but existing color evaluation methods such as visual rating are subjective, while instrument colorimetry is costly, provides limited throughput, and often struggles to accurately detect differences between genotypes. To address these challenges, we developed a high‐throughput phenotyping pipeline that integrates computer vision and deep learning to improve canning color quality assessment in dry beans. This pipeline combines the YOLOv8n (“You Only Look Once” version 8, nano variant) object detection model with the Segment Anything Model for precise bean segmentation. 525 black dry bean genotypes from Michigan State University preliminary and advanced yield trials over multiple years were evaluated using this pipeline. The final model was trained with over 1200 images of canned dry bean samples. The YOLOv8n model achieved near‐perfect detection performance, with precision reaching 0.99 and recall reaching 1 after 44 epochs of training. Comparative analysis showed that the image‐derived D ‐score (euclidean distance–based image‐derived color score) consistently outperformed visual ratings and colorimetry methods, with lower prediction error in regression models. The D ‐score metric offered high resolution in color assessment, enabling the distinction of subtle, genotype‐level differences. Additionally, the D ‐score provides a ranking criterion that enables breeders to make informed decisions and select superior genotypes. This pipeline was deployed to Michigan State University high‐performance computing center and can process 200 high‐resolution images in under 5 min, making it practical for large‐scale breeding applications while eliminating subjective bias and reducing costs.