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Related Experiment Video

Updated: May 15, 2025

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

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

Gaofei Qiao¹, Zhibin Zhang¹, Bin Niu¹

¹Key Laboratory of Wireless Networks and Mobile Computing, School of Computer Science, Inner Mongolia University, Hohhot, China.

Frontiers in Plant Science

|April 11, 2025

Summary

This study presents a novel 3D plant reconstruction and segmentation method for accurate phenotypic trait extraction. The developed PointSegNet achieves high accuracy in segmenting maize, tomato, and soybean plants for smart agriculture applications.

Keywords:

lightweight network neural radiance fields plant phenotype point cloud segmentation three-dimensional point cloud

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Last Updated: May 15, 2025

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Area of Science:

Plant Science
Computer Vision
Artificial Intelligence
Agricultural Technology

Background:

Accurate 3D plant reconstruction and organ segmentation are essential for automated phenotypic trait extraction.
Existing methods may lack efficiency or accuracy for complex plant structures.

Purpose of the Study:

To develop a robust and accurate method for 3D plant reconstruction and point cloud segmentation.
To enable precise extraction of phenotypic parameters from 3D plant models.
To advance smart agriculture through objective and reliable plant phenotyping.

Main Methods:

3D reconstruction of maize plants using the Nerfacto neural radiance field model to generate dense point clouds.
Development of a lightweight PointSegNet with Global-Local Set Abstraction (GLSA) and Edge-Aware Feature Propagation (EAFP) modules for stem and leaf segmentation.
Phenotypic parameter extraction (stem thickness, height, leaf length, width) using Principal Component Analysis (PCA).

Main Results:

PointSegNet achieved high segmentation performance (mIoU: 93.73%, Precision: 97.25%, Recall: 96.21%, F1-score: 96.73%) on maize plants.
The method demonstrated superior performance on complex structures of tomato and soybean plants.
High correlation (R² values up to 0.99) between extracted and manually measured phenotypic parameters.

Conclusions:

The proposed 3D reconstruction and PointSegNet segmentation method offers a reliable and accurate approach for plant phenotyping.
This technology has the potential to significantly boost plant phenotypic development in smart agriculture.
The study provides an objective framework for acquiring critical plant phenotypic data.