@article{Qu2026, 
author = {Fang Qu and Longhui Fang and Juncai Wang and Wei Zhang and Wentao Song and He Huang and Youqiang Sun},
title = {Fine-grained 3D rice phenotyping via multi-scale NeRF and multimodal segmentation},
year = {2026},
journal = {Plant Phenomics},
volume = {8},
number = {2},
pages = {100176},
keywords = {Rice grain, Plant phenotyping, 3D reconstruction, Neural radiance fields, 3D segmentation},
url = {https://www.sciopen.com/article/10.1016/j.plaphe.2026.100176},
doi = {10.1016/j.plaphe.2026.100176},
abstract = {Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP25 of 84.55%, outperforming mainstream algorithms.}
}