A lightweight tea bud detection model based on Yolov5.

Tea bud detection technology is highly significant in realizing the automation and intelligence of tea bud picking. However, there are still some challenges with tea bud detection technology. For example, the problems of low detection accuracy, heavy computing, and large detection model size make the technology inconducive to the deployment of mobile terminals. Thus, a lightweight tea bud detection model based on the Yolov5model was proposed in this study. Improvements were made in the following aspects: the Ghost_conv module was introduced to replace the original convolution, considerably reducing the computing and model size; the bottleneck attention module (BAM) was added to the backbone network to suppress invalid information and improve the model detection accuracy; the weighted feature fusion was used in the neck network to efficiently fuse the low-level and high-level features, helping the network to extract effective information for recognition and improve detection accuracy; and CIoU was used as...