Corn Seed Defect Detection Based on Watershed Algorithm and Two-Pathway Convolutional Neural Networks

Abstract

Corn seed materials of different quality were imaged, and a method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model. In this study, RGB and near-infrared (NIR) images were acquired with a multispectral camera to train the model, which was proved to be effective in identifying defective seeds and defect-free seeds, with an averaged accuracy of 95.63%, an averaged recall rate of 95.29%, and an F1 (harmonic average evaluation) of 95.46%. Our proposed method was superior to the traditional method that employs a one-pathway CNN with 3-channel RGB images. At the same time, the influence of different parameter settings on the model training was studied. Finally, the application of the object detection method in corn seed defect detection, which may provide an effective tool for high-throughput quality control of corn seeds, was discussed.

Code and data availability

The paper's corn seed RGB+NIR image dataset and trained Corn-seed-Net model are not publicly deposited; the data availability statement only promises raw data from the authors upon request. No public code or data URL is provided.

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