Segmentation Network for Multi-Shape Tea Bud Leaves Based on Attention and Path Feature Aggregation

Tianci Chen, Haoxin Li, Jinhong Lv, Jiazheng Chen and Weibin Wu ()
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Tianci Chen: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Haoxin Li: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jinhong Lv: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jiazheng Chen: College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Weibin Wu: National Key Laboratory of Agricultural Equipment Technology, College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 8, 1-23

Abstract: Accurately detecting tea bud leaves is crucial for the automation of tea picking robots. However, challenges arise due to tea stem occlusion and overlapping of buds and leaves, presenting varied shapes of one bud–one leaf targets in the field of view, making precise segmentation of tea bud leaves challenging. To improve the segmentation accuracy of one bud–one leaf targets with different shapes and fine granularity, this study proposes a novel semantic segmentation model for tea bud leaves. The method designs a hierarchical Transformer block based on a self-attention mechanism in the encoding network, which is beneficial for capturing long-range dependencies between features and enhancing the representation of common features. Then, a multi-path feature aggregation module is designed to effectively merge the feature outputs of encoder blocks with decoder outputs, thereby alleviating the loss of fine-grained features caused by downsampling. Furthermore, a refined polarized attention mechanism is employed after the aggregation module to perform polarized filtering on features in channel and spatial dimensions, enhancing the output of fine-grained features. The experimental results demonstrate that the proposed Unet-Enhanced model achieves segmentation performance well on one bud–one leaf targets with different shapes, with a mean intersection over union (mIoU) of 91.18% and a mean pixel accuracy (mPA) of 95.10%. The semantic segmentation network can accurately segment tea bud leaves, providing a decision-making basis for the spatial positioning of tea picking robots.

Keywords: tea bud leaves; multi-shape targets; attention mechanism; path feature aggregation; semantic segmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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