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Updated: Mar 3, 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

Deep learning-based methods for phenotypic trait extraction in rice panicles.

Zhiao Wang^1,2, Ruihang Li^1,2, Wei Li^1,2

¹Agricultural Information Institute, Chinese Academy of Agricultural Sciences/National Agricultural Science Data Center, Beijing, China.

Frontiers in Plant Science

|March 2, 2026

Summary

This summary is machine-generated.

This study introduces an automated deep learning tool for precise rice panicle phenotyping. The system accurately measures key traits like grain number and dimensions, aiding rice breeding advancements.

Keywords:

deep learning panicle traits phenotypic trait precision extraction rice

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Area of Science:

Agricultural Science
Plant Breeding
Computer Vision

Background:

Accurate measurement of rice panicle traits is crucial for improving crop yield and quality.
Traditional phenotyping methods are often labor-intensive and lack high-throughput capabilities.

Purpose of the Study:

To develop and validate a deep learning pipeline for automated, high-precision rice panicle phenotyping.
To address challenges in measuring traits like grain number, dimensions, and maturity stages, especially under occlusion.

Main Methods:

A dataset of 5300 rice panicle images was curated, encompassing diverse types and maturity stages.
A deep learning pipeline, OPG-YOLOv8, was implemented for image analysis and trait extraction.
The model was trained and validated on subsets of the image data.

Main Results:

High accuracy was achieved for panicle length extraction (R²=0.9583) and grain counting across different panicle types (R² up to 0.9799).
Accurate measurements for grain length (R²=0.8823) and grain width (MAPE=6.64%) were obtained.
The OPG-YOLOv8 model demonstrated robust performance in phenotyping.

Conclusions:

The developed automated tool offers a comprehensive solution for rice panicle phenotyping.
This technology effectively overcomes occlusion issues and bridges the gap between advanced AI models and practical breeding applications.