Maize Leaf Disease Detection Based on an Improved YOLOv11n Model

Abstract

To address the challenges in maize leaf disease detection, including large variation in lesion scales, weak texture of small targets, strong background interference, limited recall ability for blurred lesions, and computational redundancy of conventional detection heads, this paper proposes a lightweight detection algorithm based on an improved YOLOv11n. First, a multi-scale global context kernel attention module is designed, which employs GCKA-bottleneck with large-kernel attention and residual connections to enhance the deep semantic representation of multi-scale lesions. Second, a GSConv-enhanced coordinate multi-receptive attention module is constructed, which combines coordinate position awareness and multi-scale depthwise convolution. Finally, a regression-enhanced depthwise-separable decoupled detection head is proposed to decouple classification and regression tasks, and introduces depthwise separable convolution and a distributed bounding box regression. On a public dataset containing four classes, the improved model achieves an mAP@0.5 of 85.36%, a recall of 83.32%, and a precision of 86.91%, which are 3.46, 27 2.94, and 2.38 percentage points higher than those of the original YOLOv11n, respectively. Meanwhile, GFLOPs and parameter count are reduced by 27.0% and 12.4%, respectively. The proposed algorithm strikes a favorable balance between accuracy, real-time performance, and lightweight design, providing a feasible technical support for field deployment in intelligent agricultural disease monitoring systems.