TeaPoseNet: A deep neural network for tea leaf pose recognition

Abstract

The estimation of tea leaf pose is an emerging research topic. Recognising the morphological features of tea leaves can help accurately categorise, grade, and determine their level of maturity. Therefore, this study proposes a deep neural network, TeaPoseNet, to estimate tea leaf poses. The algorithm was trained and validated using a dataset of one-bud-one-leaf images of Yinghong No.9 tea leaves and was compared with four other pose estimation networks. At the same time, the contribution of TKS_NMS to the algorithm was validated through ablation experiments. The results indicate that TKS_NMS improved the EPE accuracy of pose recognition by 16.33 %. More specifically, the algorithm achieved a good overall performance, with PCK, AUC, EPE, and NME reaching 0.9800, 0.8147, 9.0955, and 0.0644, respectively. The average running speed for detecting the pose of a single tea leaf image was 40.01 ms. To the best of our knowledge, this is the first application of pose estimation technology to the detection and analysis of Yinghong No.9 tea leaves. The results show that the proposed algorithm can effectively estimate the pose of tea leaves, thus providing a reference for subsequent tea research.

Plant phenotyping relevance

茶葉の葉姿勢という植物形態形質を画像から推定する深層学習手法を開発し、データセット、比較評価、アブレーション検証、性能指標を提示しており、植物フェノタイピング手法が研究の中心である。

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