ICFMNet: an automated segmentation and 3D phenotypic analysis pipeline for plant, spike, and flag leaf type of wheat

文献类型： 外文期刊

第一作者： Xiao, Pengliang

作者： Xiao, Pengliang;Huang, Linsheng;Liang, Dong;Xiao, Pengliang;Wu, Sheng;Wen, Weiliang;Wang, Chuanyu;Lu, Xianju;Ge, Xiaofen;Li, Wenrui;Guo, Xinyu;Xiao, Pengliang;Wu, Sheng;Wen, Weiliang;Wang, Chuanyu;Lu, Xianju;Ge, Xiaofen;Li, Wenrui;Guo, Xinyu;Gao, Shiqing

作者机构：

关键词： 3D phenotypic automated analysis; Semantic segmentation; Instance segmentation; Deep learning

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 239 卷

页码：

收录情况： SCI

摘要： Three-dimensional high-throughput plant phenotyping technology offers an opportunity for simultaneous acquisition of plant organ traits at the scale of plant breeders. Wheat, as a multi-tiller crop with narrow leaves and diverse spikes, poses challenges for organ segmentation and measurement due to issues such as occlusion and adhesion. Therefore, building on previous research, this paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages. This system enables automated and precise three-dimensional phenotypic acquisition and analysis of wheat plant architecture, spike morphology, and flag leaf traits. To address the challenges posed by the significant structural differences among wheat spikes, leaves, and stems, as well as their compact spatial distribution, we propose a point cloud segmentation model based on deep learning called ICFMNet. ICFMNet relies on an instance center feature matching module, which extracts features from each instance's central region and matches them with global point-wise features by computing feature similarity. This approach enables precise instance mask generation independent of the spatial structure of the point cloud. In the analysis of wheat phenotypes, we introduce a contour-based method to accurately extract the barren segment from 3D-scale wheat spikes. Furthermore, we perform the analysis of a total of 19 phenotypes, including flag leaf phenotypes and whole-plant phenotypes. In the organ point cloud segmentation tests for wheat spikes, stems, and leaves, the semantic segmentation achieves mPrec, mRec, and mIoU values of 95.9 %, 96.0 %, and 92.3 %, respectively. The instance segmentation attains mAP and mAR scores of 81.7 % and 83.0 %, respectively. Moreover, in comparison to five other segmentation network models, ICFMNet demonstrates superior segmentation performance. To better assess barren segment localization accuracy, additional evaluations are conducted using two metrics: interval overlap and interval error, achieving values of 92.33 % and 0.1123 cm, respectively. Experimental results indicate that our method excels in terms of accuracy, efficiency, and robustness, providing a reliable systematic platform for precise identification and breeding research of wheat plant types. The source code and trained models for ICFMNet are available at https://github.com/xiao-pl/ICFMNet.

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