A diseases and pests identification system in tea leaves using improved YOLOv5 deep learning model - Deng, Xianghong; Zhou, Zhiwei; Zheng, Xuwen (Edelweiss Applied Science and Technology, 2025)
People all throughout the world enjoy tea. The traditional detection of tea diseases relies heavily on farm experts, which often takes a lot of time. Computer vision technology and artificial intelligence can automatically detect diseases and control their spread in tea leaves in real time. This study suggested a deep learning-based version of the YOLOv5 algorithm. It then improved the model structure of YOLOv5 and integrated Omni Dimensional Dynamic Convolution (ODConv) and Convolutional Block Attention Module (CBAM) attention mechanisms into YOLOv5. The findings demonstrate that the enhanced YOLOv5's accuracy is better than that of previous approaches and 5.06% greater than it was prior to the enhancement. Using the improved YOLOv5 detection algorithm to achieve a high-precision model for identifying tea pests and diseases and combining it with the Pyside6 library to design an interface recognition system, the development of the target detection recognition page is completed. In conclusion, this work offers a useful deep learning-based technique for the quick and precise diagnosis of tea disorders in the area of autonomous tea disease detection.