Abstract:Aiming to achieve rapid localization and precise detection of cucumber downy mildew under complex environmental conditions, an improved detection model named SGD-YOLO (SimAM Guide-Fusion DySample-YOLO) was proposed, based on the YOLO v8n deep learning framework. Addressing the challenges of small sample size and small target detection in cucumber downy mildew, cucumber leaf images infected by the disease were used as research objects. A data augmentation strategy was employed by combining the FT saliency detection algorithm to guide the CutMix method, and a transfer learning approach was adopted to alleviate overfitting caused by limited training data. On top of the YOLO v8n baseline, SGD-YOLO integrated a parameter-free lightweight attention module a simple, parameter-free attention module (SimAM) to enhance the propagation of key features and improve overall network performance. It also employed the dynamic lightweight upsampling module DySample to strengthen upsampling behavior and improve the detection of small diseased targets. In addition, the traditional Concat operation was replaced with the context guide fusion module (CGFM), which leveraged coordinate attention (CoordAtt) to achieve more precise multi-scale feature fusion and better lesion feature extraction. The loss function was replaced with WIoUv3, which incorporated a gradient gain allocation strategy to enhance the model’s generalization ability. Experimental results showed that the augmented dataset improved detection accuracy by 12.0 percentage points over the original dataset, and transfer learning further improved accuracy by 5.3 percentage points. The improved SGD-YOLO achieved an overall detection accuracy of 84.6% and a mean average precision (mAP) of 93.9%, outperforming the baseline model by 7.4 percentage points and 9.5 percentage points, respectively. The research result can provide a valuable reference for small-sample plant disease detection tasks in real-world agricultural applications.
