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Related Experiment Video

Updated: Jul 15, 2025

Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

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Published on: January 25, 2022

High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.

Yuwei Lu^1,2,3, Jinhu Wang¹, Ling Fu^2,3,4

¹Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China.

Frontiers in Plant Science

|October 4, 2023

Summary

This summary is machine-generated.

This study introduces a novel, high-precision method for analyzing rice grain traits using visible light scanning and deep learning. The non-destructive technique enables rapid, accurate phenotyping of on-panicle grains, improving crop breeding efficiency.

Keywords:

deep learning high-throughput phenotyping rice rice panicle traits visible light scanning

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Area of Science:

Agricultural Science
Computer Vision
Deep Learning
Plant Phenotyping

Background:

Rice is a critical global food source, necessitating advancements in breeding for high yield and quality.
Accurate measurement of rice grain traits is essential for assessing yield potential and quality.
Current phenotyping methods are often manual, time-consuming, and costly.

Purpose of the Study:

To develop a high-throughput, non-destructive phenotyping method for rice panicles.
To accurately extract critical on-panicle rice grain traits without separating or threshing.
To leverage visible light scanning and deep learning for precise trait analysis.

Main Methods:

Visible light scanning imaging of rice panicles.
Deep learning models (Faster R-CNN and Pix2Pix) for grain detection, segmentation, and occlusion compensation.
An image processing pipeline to calculate fifteen phenotypic traits.

Main Results:

High correlation (R² values 0.83–0.99) and low error (MAPE 1.65%–9.13%) for key traits compared to manual measurements.
Rapid imaging (approx. 60 seconds/panicle) and data processing (<10 seconds).
Identified varietal differences in grain length, width, and length/width ratio distributions.

Conclusions:

The developed method offers high-throughput, non-destructive, and precise extraction of on-panicle rice grain traits.
Its low cost and robust performance facilitate widespread adoption in crop breeding.
Provides novel approaches for phenotyping rice and other crop panicle traits.