Locating Tea Bud Keypoints by Keypoint Detection Method Based on Convolutional Neural Network

Yifan Cheng, Yang Li, Rentian Zhang, Zhiyong Gui, Chunwang Dong () and Rong Ma ()
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Yifan Cheng: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Yang Li: Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Rentian Zhang: Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Zhiyong Gui: Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Chunwang Dong: Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250100, China
Rong Ma: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China

Sustainability, 2023, vol. 15, issue 8, 1-16

Abstract: Tea is one of the most consumed beverages in the whole world. Premium tea is a kind of tea with high nutrition, quality, and economic value. This study solves the problem of detecting premium tea buds in automatic plucking by training a modified Mask R-CNN network for tea bud detection in images. A new anchor generation method by adding additional anchors and the CIoU loss function were used in this modified model. In this study, the keypoint detection branch was optimized to locate tea bud keypoints, which, containing a fully convolutional network (FCN), is also built to locate the keypoints of bud objects. The built convolutional neural network was trained through our dataset and obtained an 86.6% precision and 88.3% recall for the bud object detection. The keypoint localization had a precision of 85.9% and a recall of 83.3%. In addition, a dataset for the tea buds and picking points was constructed in study. The experiments show that the developed model can be robust for a range of tea-bud-harvesting scenarios and introduces the possibility and theoretical basis for fully automated tea bud harvesting.

Keywords: tea buds plucking; convolutional neural network; object detection; keypoint detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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