A lightweight deep convolutional neural network development for soybean leaf disease recognition

Yakun Zhang^1*Ruofei Bao¹Mengxin Guan¹Zixuan Wang¹Libo Wang¹Xiahua Cui²Xiaoli Niu¹Yan Wang¹Shaukat Ali³Fu Zhang¹Yafei Wang^4*

• ¹Henan University of Science and Technology, Luoyang, China
• ²College of Biological and Agricultural Engineering, Jilin University, Changchun 130025, China, Henan University of Science and Technology, Luoyang, China
• ³Wah Engineering College, University of Wah, Quaid Avenue, Wah Cantt, District Rawalpindi, Punjab, Pakistan, Henan University of Science and Technology, Luoyang, China
• ⁴School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China, Henan University of Science and Technology, Luoyang, China

Soybean is one of the world's major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats to its yield and quality during soybean cultivation. Rapid identification of soybean leaf diseases could provide a better solution for efficient control and subsequent precision application. In this study, a lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification. In MFEF-DCNet, a multiscale feature extraction fusion (MFEF) module for soybean leaves was constructed by utilizing a convolutional attention module and depth-separable convolution to improve the model feature extraction capability. Multiscale features are fused by using dense connections (DC) in the backbone network to improve the model generalization capability. Experiments were implemented on eight distinct disease and deficiency classes of soybean images (including bacterial blight, cercospora leaf blight, downy mildew, frogeye leaf spot, healthy, potassium deficiency, soybean rust, and target spot) using the proposed network. The results showed that the MFEF-DCNet had an accuracy of 0.9470, an average precision of 0.9510, an average recall of 0.9480, and an F1-score of 0.9490 for soybean leaf disease identification. And MFEF-DCNet had certain performance advantages in terms of classification accuracy, convergence speed and other effects compared with VGG16, ResNet50, DenseNet201, EfficientNetB0, Xception and MobileNetV3_small models. In addition, the accuracy of the MFEF-DCNet model in recognizing soybean diseases in local data was 0.9024, which indicated that the MFEF-DCNet model had favorable application in practical applications. The proposed model and experience in this study could provide useful inspiration for automated disease identification in soybean and other crops.