In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection predominantly focuses on single leaves with single diseases, with limited attention given to the detection of compound diseases on a single leaf. However, the occurrence of compound diseases complicates the accuracy of traditional deep learning algorithms for disease detection, necessitating the exploration of new models for the identification of compound diseases on corn leaves. To achieve rapid and accurate identification of compound diseases in corn fields, this study adopts the YOLOv5s model as the base network, chosen for its smaller size and faster detection speed. We propose a corn leaf compound disease recognition method, YOLOv5s-C3CBAM, based on an attention mechanism. To address the challenge of limited data for corn leaf compound diseases, a CycleGAN model is employed to generate synthetic images. The scarcity of real data is thereby mitigated, facilitating the training of deep learning models with sufficient data. The YOLOv5s model is selected as the base network, and an attention mechanism is introduced to enhance the network’s focus on disease lesions while mitigating interference from compound diseases. This augmentation results in improved recognition accuracy. The YOLOv5s-C3CBAM compound disease recognition model, incorporating the attention mechanism, achieves an average precision of 83%, an F1 score of 81.98%, and a model size of 12.6 Mb. Compared to the baseline model, the average precision is improved by 3.1 percentage points. Furthermore, it outperforms Faster R-CNN and YOLOv7-tiny models by 27.57 and 2.7 percentage points, respectively. This recognition method demonstrates the ability to rapidly and accurately identify compound diseases on corn leaves, offering valuable insights for future research on precise identification of compound agricultural crop diseases in field conditions.

Fusarium head blight (FHB), a severe ailment triggered by fungal pathogens, poses a considerable risk to both the yield and quality of winter wheat worldwide, underscoring the urgency for precise detection measures that can effectively mitigate and manage the spread of FHB. Addressing the limitations of current deep learning models in capturing detailed features from UAV imagery, this study proposes an advanced identification model for FHB in wheat based on multispectral imagery from UAVs. The model leverages the U²Net network as its baseline, incorporating the Coordinate Attention (CA) mechanism and the RFB-S (Receptive Field Block—Small) multi-scale feature extraction module. By integrating key spectral features from multispectral bands (SBs) and vegetation indices (VIs), the model enhances feature extraction capabilities and spatial information awareness. The CA mechanism is used to improve the model’s ability to express image features, while the RFB-S module increases the receptive field of convolutional layers, enhancing multi-scale spatial feature modeling. The results demonstrate that the improved U²Net model, termed U²Net-plus, achieves an identification accuracy of 91.73% for FHB in large-scale wheat fields, significantly outperforming the original model and other mainstream semantic segmentation models such as U-Net, SegNet, and DeepLabV3+. This method facilitates the rapid identification of large-scale FHB outbreaks in wheat, providing an effective approach for large-field wheat disease detection.