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

Bingquan CHU; Ruiyuan WU; Haijun ZHANG; Haochuan QIN; Zishun PENG; Fengle ZHU; Yong HE

doi:10.1631/jzus.B2500647

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Research Article | Updated：2026-05-15

Embedding of ripening topology into one-stage detection for tomato cluster phenotyping
Embedding of ripening topology into one-stage detection for tomato cluster phenotyping
将成熟拓扑序列嵌入单阶段检测用于串番茄表型分析
Journal of Zhejiang University-SCIENCE B Vol. 27, Issue 5, Pages: 466-481(2026)
Affiliations：

1.School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
2.College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
3.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Funds：
DOI：10.1631/jzus.B2500647
CLC：
CSTR：
Received：14 October 2025，

Revised：2026-03-12，

Published：15 May 2026
Accepted：

楚秉泉,吴瑞源,张海军等.将成熟拓扑序列嵌入单阶段检测用于串番茄表型分析[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):466-481.

Bingquan CHU, Ruiyuan WU, Haijun ZHANG, et al. Embedding of ripening topology into one-stage detection for tomato cluster phenotyping[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 466-481.
楚秉泉,吴瑞源,张海军等.将成熟拓扑序列嵌入单阶段检测用于串番茄表型分析[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):466-481. DOI： 10.1631/jzus.B2500647.

Bingquan CHU, Ruiyuan WU, Haijun ZHANG, et al. Embedding of ripening topology into one-stage detection for tomato cluster phenotyping[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 466-481. DOI： 10.1631/jzus.B2500647.

摘要

番茄成熟度的自动化评估对现代温室作业至关重要，但多变的环境条件为其准确实现带来了持续挑战。为此，本文提出了rank-aware YOLO，一种融合果实簇内自上而下成熟这一生物学先验知识的新型检测框架。该框架通过两项关键创新实现：（1）用于回归果实相对高度的高效位置敏感检测头（efficient position-aware head）；（2）修正空间序列的动态边距感知排序损失（DM-RankLoss）。在包含3500张温室采集图像的数据集上进行评估的结果表明，该模块具有良好的即插即用特性，能将多种YOLO架构的在交并比（IoU）阈值为0.50时的平均精度均值（mAP

）最高提升5.66个百分点。模型有效学习到果实簇的拓扑结构，在归一化高度平均绝对误差（H-MAE）和成对排序准确率上分别达到0.107和84.59%，同时参数量较基线减少超过10%，具备高效部署潜力。可视化分析进一步证实，模型能利用空间上下文信息有效缓解颜色模糊带来的误判。综上，本研究为农业机器人中的原位表型分析提供了一种无需额外传感器的准确且高效的解决方案。

Abstract

The automated assessment of tomato ripeness is vi

tal 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

) 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.

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