Plant stem and leaf segmentation and phenotypic parameter extraction using neural radiance fields and lightweight point cloud segmentation networks

Abstract

High-quality 3D reconstruction and accurate 3D organ segmentation of plants are crucial prerequisites for automatically extracting phenotypic traits. In this study, we first extract a dense point cloud from implicit representations, which derives from reconstructing the maize plants in 3D by using the Nerfacto neural radiance field model. Second, we propose a lightweight point cloud segmentation network (PointSegNet) specifically for stem and leaf segmentation. This network includes a Global-Local Set Abstraction (GLSA) module to integrate local and global features and an Edge-Aware Feature Propagation (EAFP) module to enhance edge-awareness. Experimental results show that our PointSegNet achieves impressive performance compared to five other state-of-the-art deep learning networks, reaching 93.73%, 97.25%, 96.21%, and 96.73% in terms of mean Intersection over Union (mIoU), precision, recall, and F1-score, respectively. Even when dealing with tomato and soybean plants, with complex structures, our PointSegNet also achieves the best metrics. Meanwhile, based on the principal component analysis (PCA), we further optimize the method to obtain the parameters such as leaf length and leaf width by using PCA principal vectors. Finally, the maize stem thickness, stem height, leaf length, and leaf width obtained from our measurements are compared with the manual test results, yielding R 2 values of 0.99, 0.84, 0.94, and 0.87, respectively. These results indicate that our method has high accuracy and reliability for phenotypic parameter extraction. This study throughout the entire process from 3D reconstruction of maize plants to point cloud segmentation and phenotypic parameter extraction, provides a reliable and objective method for acquiring plant phenotypic parameters and will boost plant phenotypic development in smart agriculture.

Plant phenotyping relevance

3D再構成、茎葉分割、形質抽出を一体化した植物フェノタイピング手法を開発し、他手法および手測定と比較検証している。

Code and data availability

The supplied blocks describe the authors' maize point cloud dataset (20 plants), the PointSegNet model, and use of public datasets (Pheno4D, Soybean-MVS, Yang et al. maize dataset), but contain no data or code availability statement, no public repository, and no author-provided URL for their dataset, model, or analysis

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