A Deep Learning Detection Algorithm for Tea Diseases

An improved deep learning algorithm based tea disease target detection was proposed. A coordinate attention mechanism was incorporated into the network, which could enable the model to refine features and focus more on disease information, thereby suppress the interference from background factors such as branches and weeds. CIoU was selected as the loss function of the model to improve the localization capabilities. Simultaneously, the target bounding boxes in the dataset was optimized through clustering techniques to obtain more accurate prior boxes. To address the issue of insufficient disease image data, a tea diseases dataset comprising six disease types was established. Experimental results showed that, compared to other algorithms, the presented method could exhibit superior performance across multiple metrics, and provide an efficient solution for the intelligent diagnosis of tea diseases.