A classification method for soybean leaf diseases based on an improved ConvNeXt model

作者：Qinghai Wu, Xiao Ma, Haifeng Liu, Cunguang Bi, Helong Yu, Meijing Liang, Jicheng Zhang, Qi Li, You Tang, G. Ye · 发表于：Scientific Reports · 年份：2023 · DOI：10.1038/s41598-023-46492-3 · 被引用次数：39 · 研究领域：Smart Agriculture and AI、Plant Pathogens and Fungal Diseases、Plant Disease Management Techniques

Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately. The improved network model consists of three parts: feature extraction, attention calculation, and classification. The dataset used was first diversified through data augmentation operations such as random masking to enhance network robustness. An attention module was then used to generate feature maps at various depths. This increased the network's focus on discriminative features, reduced background noise, and enabled the use of the LeakyReLu activation function in the attention module to prevent situations in which neurons fail to learn when the input is negative. Finally, the extracted features were then integrated using a fully connected layer, and the predicted disease category inferred to improve the classification accuracy of soybean leaf diseases. The average recognition accuracy of the improved network model for soybean leaf diseases was 85.42% both higher than the six deep learning comparison models (ConvNeXt (66.41%), ResNet50 (72.22%), Swin Transformer (77.00%), MobileNetV3 (67.27%), ShuffleNetV2 (59.89%), and SqueezeNet (72.92%)), thus proving the effectiveness of the...