Abstract
In order to achieve efficient and accurate detection of common corn leaf diseases such as leaf blight, gray spot disease, and rust, a corn leaf disease detection method based on CA-YOLOv8 was proposed. In this method, the Coordinate Attention(CA) attention mechanism was added after the feature map output from the Neck part to enhance the feature extraction capability of the model. The experimental results showed that the precision，recall and mean average precision(mAP) of the CA-YOLOv8 model on the test set were 94.08%, 90.53% and 97.38%, respectively. Compared with the YOLOv8, YOLOv8+SE and YOLOv8+CBAM models, the mAP was improved by 2.15, 0.86 and 2.35 percentage points, respectively. Compared with Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8 models, the mAP has increased by 63.53, 29.24, 3.21, and 2.15 percentage points, respectively. The study showed that the CA-YOLOv8 model can provide a technical reference for the development of a portable intelligent corn leaf disease detection system.
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