| Cotton leaf disease detection based on improved YOLO |
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| DOI：10.7606/j.issn.1000-7601.2024.06.20 |
| Key Words: cotton disease detection YOLOX-S YOLOv7 attention mechanism |
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| Abstract: |
| To achieve intelligent management of cotton and address the challenge of low accuracy in detecting cotton leaf diseases in complex environments, this paper presents results from using an improved YOLO algorithm for detecting common cotton leaf diseases. It incorporates the SE, CBAM, and ECA attention modules into the YOLOX-S and YOLOv7 algorithms to enhance the model’s focus on diseased areas, effectively suppress background noise, and significantly reduce the omission rate. Using Gradient Weighted Class Activation Mapping to generate target detection heatmaps, visualize effective features, and understand the region of interest of the model. The experimental results demonstrate that the detection performance of the YOLOX-S+SE, YOLOX-S+CBAM, and YOLOX-S+ECA models surpassed that of the YOLOX-S model, with the YOLOX-S+CBAM model achieving the best performance. Similarly, the YOLOv7+SE, YOLOv7+CBAM, and YOLOv7+ECA models outperformed the YOLOv7 model, with the YOLOv7+CBAM model delivering the best detection performance. Compared to other common object detection models, the YOLOX-S+CBAM and YOLOv7+CBAM models exhibit higher values in accuracy rate, recall rate, comprehensive evaluation index, and average accuracy than the Faster R-CNN and SSD models. A comprehensive comparison shows that the YOLOX-S+CBAM model outperforms the YOLOv7+CBAM model, striking a balance between detection speed, accuracy, and model size. It demonstrates strong real\|time detection capabilities for cotton leaf diseases in complex backgrounds. This research provides valuable guidance for the prevention and control of cotton diseases. |
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