Resumen
Existing disease detection models for deep learning-based monitoring and prevention of pepper diseases face challenges in accurately identifying and preventing diseases due to inter-crop occlusion and various complex backgrounds. To address this issue, we propose a modified YOLOv7-GCA model based on YOLOv7 for pepper disease detection, which can effectively overcome these challenges. The model introduces three key enhancements: Firstly, lightweight GhostNetV2 is used as the feature extraction network of the model to improve the detection speed. Secondly, the Cascading fusion network (CFNet) replaces the original feature fusion network, which improves the expression ability of the model in complex backgrounds and realizes multi-scale feature extraction and fusion. Finally, the Convolutional Block Attention Module (CBAM) is introduced to focus on the important features in the images and improve the accuracy and robustness of the model. This study uses the collected dataset, which was processed to construct a dataset of 1259 images with four types of pepper diseases: anthracnose, bacterial diseases, umbilical rot, and viral diseases. We applied data augmentation to the collected dataset, and then experimental verification was carried out on this dataset. The experimental results demonstrate that the YOLOv7-GCA model reduces the parameter count by 34.3% compared to the YOLOv7 original model while improving 13.4% in mAP and 124 frames/s in detection speed. Additionally, the model size was reduced from 74.8 MB to 46.9 MB, which facilitates the deployment of the model on mobile devices. When compared to the other seven mainstream detection models, it was indicated that the YOLOv7-GCA model achieved a balance between speed, model size, and accuracy. This model proves to be a high-performance and lightweight pepper disease detection solution that can provide accurate and timely diagnosis results for farmers and researchers.