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.

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Potato early blight and late blight are devastating diseases that affect potato planting and production. Thus, precise diagnosis of the diseases is critical in treatment application and management of potato farm. However, traditional computer vision technology and pattern recognition methods have certain limitations in the detection of crop diseases. In recent years, the development of deep learning technology and convolutional neural networks has provided new solutions for the rapid and accurate detection of crop diseases. In this study, an integrated framework that combines instance segmentation model, classification model, and semantic segmentation model was devised to realize the segmentation and detection of potato foliage diseases in complex backgrounds. In the first stage, Mask R-CNN was adopted to segment potato leaves in complex backgrounds. In the second stage, VGG16, ResNet50, and InceptionV3 classification models were employed to classify potato leaves. In the third stage, UNet, PSPNet, and DeepLabV3+ semantic segmentation models were applied to divide potato leaves. Finally, the three-stage models were combined to segment and detect the potato leaf diseases. According to the experimental results, the average precision (AP) obtained by the Mask R-CNN network in the first stage was 81.87%, and the precision was 97.13%. At the same time, the accuracy of the classification model in the second stage was 95.33%. The mean intersection over union (MIoU) of the semantic segmentation model in the third stage was 89.91%, and the mean pixel accuracy (MPA) was 94.24%. In short, it not only provides a new model framework for the identification and detection of potato foliage diseases in natural environment, but also lays a theoretical basis for potato disease assessment and classification.