YOLO-ALDS: an instance segmentation framework for tomato defect segmentation and grading based on active learning and improved YOLO11

Abstract

Tomato defect detection and grading based on machine vision are crucial in post-harvest operations, significantly enhancing agricultural product value and market competitiveness. However, accurate segmentation and grading of tomato surface defects remain challenging due to significant intra-class variations, imbalanced defect categories, and especially high manual annotation costs. Therefore, an instance segmentation framework (YOLO-ALDS) was proposed for tomato defect segmentation and grading automatically. The proposed framework included fast dataset preparation based on Active Learning (AL) and segmentation based on improved YOLO-DS. For the dataset preparation part, an uncertainty and diversity-driven active learning (UDAL) strategy was proposed for selecting the most informative defect samples to alleviate the annotation cost and enhance labeling efficiency. For defect segmentation, an improved YOLO11-DS segmentation model is developed by introducing Dynamic Convolution modules in the backbone network, adaptively capturing subtle variations and indistinct boundaries of tomato defects. Moreover, to specifically improve the learning capability for challenging samples with complex and ambiguous morphology selected by the UDAL, a novel SlideLoss function is integrated into the YOLO-DS model, dynamically emphasizing optimization on hard-to-segment instances. Experimental results demonstrate that the proposed YOLO-ALDS reduces manual annotation workload by over 40%, and achieves an mAP@0.5 of 84.1%, surpassing traditional YOLO11 by 0.8%, with notable performance improvements of 1.2%, 1.7%, and 3.7% for white defects, hyperplasia, and cracks, respectively. Compared to mainstream segmentation networks, our approach exhibits significant performance advantages. Furthermore, the developed intelligent tomato grading system based on our model attains practical classification accuracy exceeding 96%, highlighting its promising potential for cost-effective and efficient agricultural automation.

Plant phenotyping relevance

トマト表面欠陥を画像からセグメンテーションし、欠陥状態と等級を推定する手法の開発・評価が中心であり、植物状態の観測手法に該当する。

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