Tea bud pose estimation and grading detection network based on improved YOLOv7.

Abstract

Intelligent recognition and rapid grading of tea buds are crucial for advancing tea-picking machinery; however, complex plantation backgrounds and inconsistent bud growth have limited traditional algorithms to merely identifying picking points, neglecting bud pose and grade, which restricts harvesting efficiency. To address these challenges, we propose YOLO-PC, a deep neural network designed for simultaneous tea bud pose estimation and classification, which incorporates a dynamic snake convolution (DSConv) module for enhanced shape feature extraction, an ELASPP-CSPC attention mechanism for improved spatial pooling, and EIoU loss to accelerate regression and boost localization accuracy. Experimental results demonstrate that the model achieves detection accuracies of 91.5% for one-bud-one-leaf and 93.2% for one-bud-two-leaf scenarios, with an average keypoint detection accuracy (Pose_mAP) of 89.7% and a Normalized Mean Error (NME) of 0.047; furthermore, compared to YOLOv7-pose, it increases mean average precision by 7.26% and pose accuracy by 9.65% while reducing parameters by 14.99 M. Ablation studies confirm the superior performance of the proposed model in tea bud detection, indicating its potential to provide robust practical support for adaptive and intelligent tea harvesting systems.

Code and data availability

The paper describes a custom tea bud dataset (1,770 images of 'Longjing 43' and 'Zhongcha 108' with keypoint annotations) and the YOLO-PC model, but no public dataset, code, or model deposit is provided. The data availability statement only promises raw data from the authors upon request, and no public URL is given.

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