LKCAFormer: A Lightweight Transformer with Large-Kernel Cooperative Attention for the Segmentation of Field Maize Leaf Diseases

In smart agriculture, segmentation models are essential for the early and accurate detection of diseases. However, the complex backgrounds and diverse diseases on maize leaves present significant challenges. Although current models have improved, these advancements often lead to larger model sizes and higher computational demands, making them difficult to deploy on hardware with limited resources. To overcome these issues, we propose a new lightweight segmentation network called LKCAFormer. This network is specifically designed for accurate maize leaf disease segmentation and is built upon a coordinated attention mechanism and cross-scale large-kernel convolutions. Our approach introduces the Large-Kernel Convolution Cooperative Attention (LK-COA) module, which uses large-kernel convolutions to extract global features and a cooperative attention mechanism to capture fine details of small spots. This combination enhances the segmentation of small spots and reduces errors caused by spot adhesion. Additionally, the CSDecoder effectively fuses shallow features, rich in edge and detail information, with deeper semantic features to produce precise segmentation results. Experimental results on three maize leaf disease datasets demonstrate that our method outperforms existing segmentation techniques, confirming its effectiveness in the pathological analysis of maize leaf diseases.