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

Beneficial Effects of Spermidine on Ovarian Function, Gut Microbiota Composition, and Associated Metabolic Changes.

Nutrients·2026

Same author

Effects of interlayer cations and hydration on PFAS adsorption and mobility in montmorillonite: A molecular dynamics study.

Journal of contaminant hydrology·2026

Same author

Correction: A new criterion for defining tunnel portal failure using the strength reduction method.

PloS one·2026

Same author

Spermidine Targets Ovarian Granulosa Cells via Activating the FHC/SLC7A11 Axis to Regulate Iron Homeostasis and Ameliorate Iron Overload-Induced Ovarian Dysfunction.

Antioxidants (Basel, Switzerland)·2026

Same author

Two Molecularly Defined Neuronal Types in the Mammillary Body Govern Different Temporal Periods during Working Memory Maintenance.

Research (Washington, D.C.)·2026

Same author

Factors associated with device-associated health care-associated infections after cardiac surgery: a matched cohort study.

PeerJ·2026

Same journal

Machine-learning-assisted comparative analysis of rice growth and yield formation in field and plant factory systems.

Frontiers in plant science·2026

Same journal

TomatoweedDet: a real-field multi-class weed detection dataset and YOLO benchmark for tomato production systems.

Frontiers in plant science·2026

Same journal

Genomic advances in orphan and underutilized Brassicaceae crops and their wild relatives.

Frontiers in plant science·2026

Same journal

Delayed sowing limits grain number per spike in wheat by restricting young spike differentiation through reduced photothermal resources.

Frontiers in plant science·2026

Same journal

Deterministic assembly and centralized networks define the <i>Pinus massoniana</i> rhizosphere mycobiota.

Frontiers in plant science·2026

Same journal

Cover cropping enhances fruit quality in protected citrus cultivation by modulating rhizosphere microbiome and iron availability.

Frontiers in plant science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Published on: September 22, 2023

CDPNet: a deformable ProtoPNet for interpretable wheat leaf disease identification.

Jinyu Zeng¹, Bingjing Jia¹, Chenguang Song¹

¹College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, Anhui, China.

Frontiers in Plant Science

|November 24, 2025

Summary

This summary is machine-generated.

A new wheat leaf disease identification model, CDPNet, improves accuracy by leveraging contrastive learning and attention mechanisms. This advanced computer vision approach enhances disease detection in field conditions.

Keywords:

Barlow Twins CDPNet Cross Attention identification of wheat leaf diseases interpretability

More Related Videos

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Published on: September 22, 2023

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

Published on: June 7, 2024

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate wheat leaf disease identification is vital for food security.
Existing computer vision models face challenges with scattered lesions and lack interpretability.

Purpose of the Study:

To develop an interpretable computer vision model for wheat leaf disease identification.
To improve disease recognition accuracy in field conditions.

Main Methods:

Proposed the Contrastive Deformable Prototypical part Network (CDPNet).
Utilized Cross Attention (CA) for enhanced feature discriminability.
Employed Barlow Twins self-supervised contrastive learning to address data scarcity.

Main Results:

Achieved an average recognition accuracy of 95.83% on the wheat leaf disease dataset.
Outperformed the baseline model by 2.35%.

Conclusions:

CDPNet offers superior performance for real-world wheat disease identification.
The model provides clinically interpretable decision support.