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

作者：Yakun Zhang, Ruofei Bao, Mengxin Guan, Zixuan Wang, Libo Wang, Xiahua Cui, X. Y. Niu, Yan Wang, Yan Wang, Shaukat Ali, Yafei Wang, Yafei Wang · 发表于：Frontiers in Plant Science · 年份：2025 · DOI：10.3389/fpls.2025.1655564 · 被引用次数：3 · 研究领域：Smart Agriculture and AI、Plant Disease Management Techniques、Plant Pathogens and Fungal Diseases

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 perform...