Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Erratum to: Distinct immune escape and microenvironment between RG-like and pri-OPC-like glioma revealed by single-cell RNA-seq analysis.

MedScience·2026

Same author

Coformer: a deep learning-based framework for cross-environment and multi-year cotton phenotype prediction and interpretation.

Plant methods·2026

Same author

Multicenter clinical application of regulatory dual-channel digestive tract reconstruction with pylorus-preserving approach in advanced gastric cancer.

Frontiers in oncology·2026

Same author

Mechanical Properties and Parameter Optimization for the "Suitable Harvest" Stage of Vegetable Sweet Potato Shoot Tips in Mechanized Harvesting.

Plants (Basel, Switzerland)·2026

Same author

XooNet: a high-throughput UAV-based approach for field screening of bacterial blight-resistant germplasm in wild rice.

Frontiers in plant science·2026

Same author

Paft-wpest: wolfberry pests fine-grained classification method based on generative self-supervised learning.

Plant methods·2026

Same journal

LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.

Frontiers in plant science·2026

Same journal

Patterns of plastid gene evolution: identifying candidate genes for plastid-nuclear incompatibility across the Campanulaceae.

Frontiers in plant science·2026

Same journal

Assembly and comparative analysis of the complete mitochondrial genome of <i>Holmskioldia sanguinea</i>.

Frontiers in plant science·2026

Same journal

Genotypic resilience and fruit quality responses of tomato (<i>Solanum lycopersicum</i> L.) in progressive salinity stress across diverse cultivation conditions.

Frontiers in plant science·2026

Same journal

Growth history revealed by tree rings provides clues for the conservation of an endangered subtropical tree species.

Frontiers in plant science·2026

Same journal

Climate change reshapes habitat suitability of ancient tea trees in Yunnan: insights from an optimized MaxEnt model.

Frontiers in plant science·2026

See all related articles

Search research articles

Related Experiment Video

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

More Related Videos

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Related Experiment Videos

Last 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

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

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.