Corn Kernel Segmentation and Damage Detection Using a Hybrid Watershed-Convex Hull Approach.

Abstract

Accurate segmentation of adhered (sticky) corn kernels and reliable damage detection are critical for quality control in corn processing and kernel selection. Traditional watershed algorithms suffer from over-segmentation, whereas deep learning methods require large annotated datasets that are impractical in most industrial settings. This study proposes W&C-SVM, a hybrid computer vision method that integrates an improved watershed algorithm (Sobel gradient and Euclidean distance transform), convex hull defect detection and an SVM classifier trained on only 50 images. On an independent test set, W&C-SVM achieved the highest damage detection accuracy of 94.3%, significantly outperforming traditional watershed SVM (TW + SVM) (74.6%), GrabCut (84.5%) and U-Net trained on the same 50 images (85.7%). The method effectively separates severely adhered kernels and identifies mechanical damage, supporting the selection of intact kernels for quality control. W&C-SVM offers a low-cost, small-sample solution ideally suited for small-to-medium food enterprises and breeding laboratories.

Plant phenotyping relevance

トウモロコシ粒の接着分離と機械的損傷という種子・植物器官の状態を、画像処理と分類器で抽出する手法を開発・比較検証しており、表現型取得が中心である。

Code and data availability

The article describes a corn kernel image dataset (50 annotated images), an SVM model, and a W&C-SVM pipeline, but contains no data availability statement, no public repository deposit, and no author code/workflow URL. The only URLs present are ORCID profiles and the CC BY license, none of which host paper-specific pha

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