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A high-precision segmentation method for rubber tree stone cells

文献类型： 外文期刊

作者： Pan, Meixi 1 ; Zhou, Guoxiong 1 ; Zhang, Yuanyuan 2 ; Yu, Yunlong 1 ; Yang, Jin 1 ; Zhao, Tianrui 1 ; Liu, Genhua 1 ;

作者机构： 1.Cent South Univ Forestry & Technol, Changsha 410004, Hunan, Peoples R China

2.Chinese Acad Trop Agr Sci, Rubber Res Inst, State Ctr Rubber Breeding, Haikou 571101, Hainan, Peoples R China

关键词： Rubber tree stone cells; low-rank KAN module; wave-SC module; CGWO optimization algorithm

期刊名称：PLANT JOURNAL （ 影响因子：5.7； 五年影响因子：7.0 ）

ISSN： 0960-7412

年卷期： 2025 年 123 卷 3 期

页码：

收录情况： SCI

摘要： Stone cells constitute a significant portion of rubber tree bark and are associated with key traits, including bark cracking, hardness, stress resistance, and latex yield. Lack of a fast and accurate method to identify stone cells in rubber tree bark and further for quantifying distribution and area proportion restricts the study of stone cells in the bark of the rubber tree. We propose an automatic segmentation network for rubber tree stone cells based on image recognition, termed CGWO-LWNet. This network addresses challenges such as complex edges, regional distribution patterns, and the instability of traditional segmentation networks during training. Firstly, we introduce a low-rank KAN module to reshape neural network learning, facilitating information sharing and feature fusion between encoders, improving edge segmentation accuracy. Secondly, we design a wavelet attention mechanism, Wave-SC, to capture the distribution patterns of stone cells in rubber tree bark slices. Finally, we propose a new gray wolf constrained optimization algorithm (CGWO) to enhance network training stability. To optimally train CGWO-LWNet, we constructed a dataset of 1084 rubber tree stone cell images from CATAS and conducted experiments. Experimental results show that CGWO-LWNet achieves 69.1% MIoU, 81.7% DSC coefficient, and 80.4% recall on the dataset. Compared to other algorithms, CGWO-LWNet demonstrates higher accuracy, achieving 97.8% in rubber tree bark stone cell segmentation. Our approach offers a practical and robust tool for high-precision segmentation of stone cells, enabling large-scale, accurate trait analysis and facilitating further genetic studies on their development and influence on latex yield, bark integrity, and stress resilience.

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