Accurate recognition and segmentation of northern corn leaf blight in drone RGB Images: A CycleGAN-augmented YOLOv5-Mobile-Seg lightweight network approach

Abstract

Northern corn leaf blight seriously threatens the health of maize crops in Northeast China. The complexity of field environments, coupled with variations in lighting conditions, poses significant challenges for accurate recognition and segmentation of this disease. To address these issues, this study employs CycleGAN networks and other methods to enhance the diversity of the dataset and proposes a lightweight neural network, Yolov5-Mobile-Seg, for the recognition and segmentation of lesion areas caused by Northern corn leaf blight. The Yolov5-Mobile-Seg network uses Mobilev2 as the backbone, integrating the Convolutional Block Attention Module (CBAM) and Fused MobileNet Bottleneck Convolution Module (FusedMBConv). This design enhances the network’s ability to capture critical information from images while minimizing the number of parameters. Additionally, by incorporating the Free Anchors mechanism, the algorithm’s adaptability to varying sizes of lesion areas is enhanced. Experimental results show that this network outperforms other approaches in identifying northern corn leaf blight, achieving an average precision (AP) of 88.8% in the recognition task and 88.0% in the segmentation task. Compared to the original network, the proposed network reduces the number of parameters by 30.6%, while improving the AP of both the recognition and segmentation tasks by 5.1%. This approach facilitates accurate recognition and efficient segmentation of lesion areas, significantly enhancing the precision and speed of damage assessment for northern corn leaf blight in maize fields.