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

TL;DR

This paper introduces a new computer vision method to accurately separate sticky corn kernels and detect damage, using a small dataset suitable for small food enterprises.

Contribution

The novel hybrid W&C-SVM method combines improved watershed, convex hull, and SVM for effective segmentation and damage detection with minimal data.

Findings

W&C-SVM achieved 94.3% damage detection accuracy, outperforming other methods.

The method effectively separates severely adhered corn kernels.

It requires only 50 training images, making it suitable for small-sample settings.

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.

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