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

Surgical Outcomes of Perioperative Toripalimab in Stage III Resectable Non-Small Cell Lung Cancer: Post Hoc Analysis of the Neotorch Randomized Clinical Trial.

JAMA surgery·2026

Same author

Tegoprazan-based dual therapy versus bismuth-containing quadruple therapy for Helicobacter pylori eradication: a prospective, multicenter, open-label, non-inferiority, randomized controlled trial.

Scientific reports·2026

Same author

CDK8 coordinates jasmonate-induced immunity with sulfur-responsive defense in <i>Arabidopsis</i>.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Tunable and Selective Doping Modulation in Pd-Filled Carbon Nanotube Transistors.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Curcumin alleviates cold stress-induced myocardial injury by regulating macrophage polarization through the STUB1/STAT1-STAT6-KLF4 signaling axis.

International immunopharmacology·2026

Same author

Large Language Model-Informed Dual-Track AI Framework for the Synergistic Design of Crack-Free and High-Strength Superalloys.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Search research articles

Related Experiment Video

Updated: Oct 6, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning.

Lili Li¹, Jiangwei Qiao², Jian Yao^1,3

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.

|January 14, 2022

Summary

Researchers developed a new method using deep learning and drone imagery to identify freezing-tolerant winter rapeseed. This automated approach significantly outperforms traditional methods, improving crop breeding efficiency and reducing economic losses from frost damage.

Keywords:

Deep learning Freezing injury recognition Machine learning Rapeseed UAV images

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Related Experiment Videos

Last Updated: Oct 6, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Area of Science:

Agricultural Science
Computer Science
Plant Breeding

Background:

Freezing injury significantly impacts winter rapeseed yield and causes economic losses.
Current methods for identifying freezing-tolerant rapeseed are inefficient, laborious, and expert-dependent.

Purpose of the Study:

To develop an automated, low-cost method for recognizing freezing-tolerant winter rapeseed materials.
To overcome the limitations of traditional field investigation methods.

Main Methods:

Utilized deep learning algorithms for binary classification of freezing-tolerant rapeseed.
Employed images captured by a consumer unmanned aerial vehicle (UAV).
Trained and evaluated five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50, GoogLeNet) on a manually constructed dataset.

Main Results:

All five deep learning networks achieved over 92% accuracy.
ResNet50 demonstrated the highest accuracy at 93.33%.
Deep learning methods significantly outperformed traditional machine learning approaches.

Conclusions:

It is feasible to recognize freezing-tolerant rapeseed using UAV imagery and deep learning.
The proposed method offers an efficient and accurate alternative for crop breeding.
This technology can aid in identifying superior winter rapeseed varieties for improved crop resilience.