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

Abstract

The internal quality assessment of potato tubers is a crucial task in agro-industrial 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 industrial sorting lines. 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 90\% and precision near 100\% 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AIモデルで検出・分割する方法が研究の中心であり、性能評価も行っているため。

Code and data availability

The supplied blocks describe a labeled dataset of over 6000 annotated potato slice images and a hybrid YOLO/ResNet/SAM/VGG16 pipeline, but contain no public deposit, availability statement, or authors' URL for the dataset, images, code, or trained models. The only URLs present are citations to prior work, which are not

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