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FACNet: A high-precision pumpkin seedling point cloud organ segmentation method

文献类型： 外文期刊

作者： Liu, Zerui 1 ; Zhao, Junhong 3 ; Hu, Yaowen 2 ; Li, Rui 1 ; Deng, Qiaomei 1 ; Guan, Renxiang 2 ; Yang, Ruoli 4 ; Xu, Zhuonong 1 ; Zhou, Guoxiong 1 ;

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

2.Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China

3.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou, Peoples R China

4.Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China

关键词： Pumpkin seedling point cloud organ; segmentation; FACNet; FBFE; AMSF; CPSAO

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 231 卷

页码：

收录情况： SCI

摘要： Accurately segmenting plant organs in pumpkin seedling point clouds is crucial for automated plant phenotyping and is essential for improving cultivation efficiency and optimizing breeding strategies. The segmentation of pumpkin seedling organs in point clouds presents challenges such as leaf overlap, indistinct boundaries between stems and leaves, and the morphological diversity of leaves and stems. To address these issues, we propose a high-precision pumpkin seedling point cloud organ segmentation network and construct, for the first time, a labeled dataset for pumpkin seedling point cloud organ segmentation. Firstly, to tackle the difficulties caused by leaf overlap and ambiguous stem-leaf boundaries, we present the Fused Bilinear Feature Extractor (FBFE). This method utilizes bilinear operations to combine local and global features, precisely capturing subtle feature differences at overlapping leaves and stem-leaf junctions. Secondly, to address the challenge of morphological diversity between leaves and stems, we introduce the Adaptive Multi-Scale Feature Fusion Module (AMSF). This module automatically adjusts feature fusion strategies across different scales, effectively integrating information from various levels and enhancing the model's ability to handle morphological diversity and capture fine details. Finally, we propose the Chebyshev Particle Snow Ablation Optimizer (CPSAO) to optimize the learning rate, improving the model's convergence speed and segmentation accuracy. Experimental results show that FACNet achieves 95.06 % mIoU, 96.87 % mPrec, 98.02 % mRec, and 97.44 % mF1 on the pumpkin seedling point cloud segmentation dataset. Compared to popular point cloud segmentation models, FACNet offers superior precision and stability in segmenting organs from pumpkin seedling point clouds.

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