Hybrid AI Pipeline for Laboratory Detection of Internal Potato Defects Using 2D RGB Imaging.

Abstract

The internal quality assessment of potato tubers is a crucial task in agro-laboratory processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in laboratory. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 95% and precision near 97.2% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware.

Plant phenotyping relevance

ジャガイモ塊茎の内部欠陥という植物器官の状態を、RGB画像と複数の画像解析モデルで検出・分割する手法の開発および性能評価が中心である。

Code and data availability

The paper's potato defect image dataset and code are not publicly available; the authors state they are available only on request due to confidentiality agreements.

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