Embedding of ripening topology into one-stage detection for tomato cluster phenotyping.

Abstract

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 (mAP 50 ) 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.

Plant phenotyping relevance

トマト果実の熟度・クラスター内位置関係を推定するYOLOベースの画像解析法を開発し、データセットで性能評価しており、フェノタイピング手法が中心である。

Code and data availability

The paper's 3500-image greenhouse tomato dataset, annotations, and code are not publicly deposited; the Data Availability Statement says data are available only upon request from the authors. The two allowed URLs are cited references (a Microsoft Research report and FAOSTAT), not paper-specific assets.

No evidence-backed public reproduction asset is currently recorded.