摘要The automated assessment of tomato ripeness is vital for modern greenhouse operations,yet challenges remain due to variable environmental conditions.To provide a solution,we propose rank-aware You Only Look Once(YOLO),a novel detection framework that incorporates the biological prior of top-to-bottom ripening within fruit clusters.This is achieved through two key innovations:an efficient position-aware head for regressing relative height for fruits and a dynamic margin-aware ranking loss(DM-RankLoss)that enforces the correct spatial sequence.Evaluated on a 3500-image dataset from a solar greenhouse,our plug-and-play module could boost the mean average precision(mAP)at intersection over union(IoU)threshold of 0.50(mAP50)of multiple YOLO architectures by up to 5.66 pecentage points.The model effectively learns the cluster topology,achieving a height-mean absolute error(H-MAE)of 0.107(normalized)and a pairwise ranking accuracy(PRA)of 84.59%,while it reduces the parameter count by over 10%compared to the baseline for efficient deployment.Visualizations confirm that the model leverages spatial context to resolve color ambiguities.Our work offers a sensor-free,accurate,and efficient solution for in situ phenotyping in agricultural robotics.