Tea Bud Recognition Method Based on Improved YOLOX Model

Objective The recognition of tea buds is one of the core technologies to realize automatic tea picking.The growth posture of tea buds and the shooting angle during collecting images will bring difficulties to the recognition of tea buds, resulting in the problem of low recognition accuracy. The study is conducted to improve the difficulty in tea bud recognition and improve the recognition accuracy of the model.

Method The study presents an improved YOLOX tea bud detection algorithm SS-YOLOX, which can accurately identify and classify tea buds including one bud with one leaf and one bud with two leaves. In this method, the feature extraction ability of the model is improved by adding the attention module(SE), the problem of missing detection of small targets is improved, the soft NMS algorithm is introduced to improve the scoring mechanism when the detection frame overlap is high, and the ability of the model to recognize the buds in different scenes is improved.

Result The ablation test of YOLOX model shows that the detection accuracy of the model can be improved by introducing soft NMS algorithm and SE module, but the improvement result is more obvious by introducing SE module. The feasibility and accuracy of the algorithm are verified by comparing different bud images. The experimental results show that the average accuracy mAP of SS-YOLOX model is 2.2% higher than that of the original YOLOX model, reaching 86.3%, indicating that the recognition ability of the model is improved after the improvement. When the number of target buds is large, SSYOLOX model can effectively reduce the missed detection rate and false detection rate.

Conclusion Therefore, SS-YOLOX model can accurately identify tea buds, and the recognition effect is better, which can provide a technical basis for intelligent tea picking.