Identification of maize leaf diseases by using the SKPSNet-50 convolutional neural network model

• Published in: Sustainable computing informatics and systems Vol. 35; p. 100695
• Main Authors: Zeng, Weihui, Li, Haidong, Hu, Gensheng, Liang, Dong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2022
• Subjects: Accurate identification; Convolutional neural network; Early maize leaf diseases; Natural scene images; SKPSNet-50; Accurate identification; SKPSNet-50; Convolutional neural network; Natural scene images; Early maize leaf diseases
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2022.100695

•A new network model SKPSNet-50 is proposed for the identification of early maize leaf diseases.•Swish_B is used instead of the ReLU function to improve the ability to extract early maize leaf disease features.•The combined Focal Loss function is proposed to solve the problem of unbalanced samples of maize leaf diseases. Maize leaf disease is the main factor that affects the yield and quality of maize. Early diagnosis and control of maize leaf diseases help ensure maize yield and quality. However, early maize leaf diseases show small spots and irregular shapes in natural scene images, resulting in current models having difficulty identifying maize leaf diseases accurately. This study proposes a SKPSNet-50 convolutional neural network model to realize the accurate identification of maize leaf diseases in natural scene images. The proposed model replaces the 3 × 3 convolution kernel in the backbone network ResNet-50 with Select Kernel–Point–Swish_B (SKPS), which is an improved building block of the selected kernel (SK) unit, and replaces the ReLU activation function with the Swish_B activation function to improve the feature extraction capability of diseased leaves with small spots and irregular shapes. In addition, this study adopts the combined focal loss function to guide the parameter adjustment of the model to solve the problem of data imbalance. Experimental results show that compared with classic machine learning models (such as BP neural network and support vector machine) and deep neural network models (such as AlexNet, VGG16, ResNet-50, SENet-50, SKNet-50 and GhostNet), the proposed model is more effective in the identification of maize leaf diseases in natural scene images, with an average identification accuracy of 92.9%, which is about 6% higher than that of the SKNet-50 model.