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 journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 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

A lightweight model and corn leaf disease recognition.

Lujie Bai¹, Shaoqiu Zhu¹, Haitao Gao^1,2

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

|November 17, 2025

Summary

This summary is machine-generated.

A new lightweight corn disease recognition model, ES-ShuffleNetV2, improves accuracy to 97.07% and reduces model size by over 30%. This advancement enhances disease prevention and production efficiency for this vital food crop.

More Related Videos

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

Author Spotlight: Investigating Fungal Pathogenicity Mechanisms in Maize

Author Spotlight: Investigating Fungal Pathogenicity Mechanisms in Maize

Published on: September 15, 2023

Related Experiment Videos

Last Updated: Jan 11, 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

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

Author Spotlight: Investigating Fungal Pathogenicity Mechanisms in Maize

Author Spotlight: Investigating Fungal Pathogenicity Mechanisms in Maize

Published on: September 15, 2023

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Corn is a globally significant food crop, yet vulnerable to diseases.
Current disease detection methods face challenges with computational cost and feature extraction.
Efficient and accurate identification is crucial for disease management and crop yield.

Purpose of the Study:

To develop a lightweight corn leaf disease recognition model for improved accuracy and efficiency.
To meet the demands of portable devices for real-time disease detection.
To enhance corn disease prevention and control strategies.

Main Methods:

Proposed a novel ES-ShuffleNetV2 model, integrating Spatial Group-wise Squeeze-and-Excitation (SGSE) blocks and Exponential Linear Unit (ELU) activation.
Implemented layer pruning to reduce model complexity and enhance mobile device efficiency.
Evaluated model performance against existing methods using accuracy and F1-Score metrics.

Main Results:

The ES-ShuffleNetV2 model achieved a recognition accuracy of 97.07%, outperforming the base model (95.43%).
Post-pruning, the model size decreased by 30.45% in parameters and 30.26% in FLOPs.
The model demonstrated superior performance in accuracy and F1-Score compared to other leading models.

Conclusions:

The ES-ShuffleNetV2 model offers an effective and efficient solution for corn leaf disease identification.
The lightweight design makes it suitable for deployment on portable devices, aiding in field-level disease management.
This research provides a foundation for advanced intelligent systems in agriculture.