动态尺度特征挖掘与双向注意力交互的黄瓜病害检测OA

Aiming to address the challenges of difficult-to-distinguish and inefficiently recognized disease spots in cucumber disease detection,a cucumber disease detection algorithm combining dynamic scale feature mining and bidirectional attention interaction was proposed.In the feature extraction stage,receptive field and channel attention convolution(RFCAConv)was introduced to enhance the feature representation of disease spot areas and improve the model's ability to recognize disease targets with significant morphological variations.An adaptive feature enhancement module(AFEM)was employed to effectively suppress background noise and enhance the distinction between targets and backgrounds.The C3k2-DSC module was designed to effectively reduce the loss of feature information caused by target shape variations.Ablation experiments conducted on a constructed cucumber disease dataset showed that the mean average precision reached 85.9%,an improvement of 3.8 percentage points compared with that of the baseline model,and the recall rate was increased by 5.1 percentage points.All improvements enhanced the model's recognition performance,with the AFEM module showing the most significant improvement.The model was tested on a public dataset containing 3 070 images,and the experimental results showed that the precision and recall rates were increased by 2.3 percentage points and 4.4 percentage points,respectively.When deployed on an Android mobile phone,the test results showed an average recognition accuracy of 84.8%and an average detection time of 0.352 s.The established cucumber disease detection system achieved accurate and rapid identification of cucumber diseases,providing an effective method for timely prevention and control of cucumber diseases.