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Related Experiment Video

Updated: Apr 28, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

IPENS: Interactive unsupervised framework for rapid plant phenotyping extraction via NeRF-SAM2 fusion.

Wentao Song^1,2, He Huang¹, Fang Qu^1,2

¹Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.

Plant Phenomics (Washington, D.C.)

|April 27, 2026

Summary

This study introduces IPENS, an interactive unsupervised method for plant phenotyping. It enables rapid, non-invasive extraction of grain-level point clouds for traits like leaf area and volume, accelerating intelligent breeding.

Keywords:

3D instance segmentation NeRF Rice and wheat phenotype SAM2 Unsupervised

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Last Updated: Apr 28, 2026

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Published on: January 9, 2026

Area of Science:

Agricultural Science
Computer Vision
Plant Biology

Background:

Advanced plant phenotyping is crucial for crop improvement and intelligent breeding.
Existing methods often require extensive manual annotation, which is challenging for diverse species and self-occluded grain-level objects.
Unsupervised methods struggle with precise segmentation of individual plant parts.

Purpose of the Study:

To develop an unsupervised, interactive method for accurate multi-target point cloud extraction in plant phenotyping.
To overcome limitations of existing methods in handling species diversity and self-occlusion at the grain level.
To enable efficient and non-invasive phenotypic trait estimation for crops like rice and wheat.

Main Methods:

Proposed IPENS (Interactive Plant Extraction using Neural Fields), an unsupervised multi-target point cloud extraction method.
Utilized radiance field information to lift 2D masks from Segment Anything Model 2 (SAM2) into 3D space.
Implemented a multi-target collaborative optimization strategy for segmenting multiple objects from single interactions.

Main Results:

Achieved high segmentation accuracy on rice (mIoU 63.72%) and wheat (mIoU 89.68%).
Demonstrated excellent phenotypic trait estimation: grain volume R² up to 0.7697 (rice) and 0.9956 (wheat).
Leaf area, length, and width predictions showed high accuracy (R² up to 0.84 for rice, 1.00 for wheat).

Conclusions:

IPENS provides a rapid (under 3 minutes), non-invasive solution for grain-level point cloud extraction without manual annotation.
The method effectively extracts phenotypic traits for rice and wheat, significantly aiding intelligent breeding programs.
IPENS offers a robust and scalable approach for advanced plant phenotyping, addressing key challenges in current technologies.