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

The subpleural pulmonary microvasculature in newborn yak (Bos grunniens).

Veterinary research communications·2008

Same author

Experimental confirmation of potential swept source optical coherence tomography performance limitations.

Applied optics·2008

Same author

A germin-like protein gene family functions as a complex quantitative trait locus conferring broad-spectrum disease resistance in rice.

Plant physiology·2008

Same author

[Spatial and temporal changes of palatal cell proliferation and cell apoptosis of retinoic acid induced mouse cleft palate in different embryonic stages].

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology·2008

Same author

Identification of an Atlantic salmon IFN multigene cluster encoding three IFN subtypes with very different expression properties.

Developmental and comparative immunology·2008

Same author

Non-Gaussian statistics and superdiffusion in a driven-dissipative dusty plasma.

Physical review. E, Statistical, nonlinear, and soft matter physics·2008

Search research articles

Related Experiment Video

Updated: Oct 14, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Fine-Grained Grape Leaf Diseases Recognition Method Based on Improved Lightweight Attention Network.

Peng Wang^1,2,3, Tong Niu^1,2,3, Yanru Mao^1,2,3

¹College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry (A&F) University, Yangling, China.

Frontiers in Plant Science

|November 8, 2021

Summary

This study introduces ECA-SNet, a lightweight deep learning model for accurately detecting grape leaf diseases in real-time. The model offers high accuracy with significantly reduced computational costs, aiding efficient disease control in orchards.

Keywords:

attention mechanism diseases recognition fine-grained image grape leaf diseases lightweight

More Related Videos

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

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

Related Experiment Videos

Last Updated: Oct 14, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

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

Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Grape leaf disease monitoring is crucial for the grape industry.
Manual methods are inefficient and labor-intensive.
Classical deep learning models require substantial computational resources, hindering practical deployment.

Purpose of the Study:

To develop an efficient, lightweight model for real-time dynamic monitoring of orchard grape leaf diseases.
To address the computational limitations of existing deep learning models for disease detection.

Main Methods:

A dataset of 6,867 grape leaf images covering five common diseases and healthy samples was curated.
Image augmentation techniques were employed to create training, validation, and test sets.
A lightweight model, ECA-SNet, was developed using ShuffleNet-v2 as a backbone, incorporating a cross-channel interactive attention mechanism to enhance fine-grained feature extraction.

Main Results:

The ECA-SNet model achieved a recognition accuracy of 98.86%, a 3.66 percentage point increase over ShuffleNet-v2.
The model has significantly fewer parameters (24.6% of ShuffleNet-v2 1.0×) and low FLOPs (37.4 M).
The average F1-score reached 0.988, demonstrating strong stability and anti-interference capabilities despite high feature similarity among diseases.

Conclusions:

The proposed lightweight attention mechanism model (ECA-SNet) efficiently diagnoses grape leaf diseases using fine-grained image information.
ECA-SNet offers a practical solution for real-time disease monitoring with low computational cost.
This advancement supports the healthy and stable development of the grape industry through improved disease control efficiency.