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

Enhancing security in financial transactions: a novel blockchain-based federated learning framework for detecting counterfeit data in fintech.

PeerJ. Computer science·2024

Same author

Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches.

Sensors (Basel, Switzerland)·2024

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

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

An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart

Muhammad Farrukh Shahid¹, Tariq J S Khanzada^2,3, Muhammad Ahtisham Aslam⁴

¹FAST School of Computing, National University of Computer & Emerging Sciences, Karachi, 75030, Pakistan. mfarrukh.shahid@nu.edu.pk.

|July 14, 2024

Summary

Early detection of cotton plant diseases using deep learning (DL) and computer vision (CV) significantly improves crop yield and economic outcomes. Continuous Wavelet Transform (CWT) features enhanced DL model performance for disease identification.

Keywords:

Agriculture Artificial intelligence CWT Cotton crops Crop monitoring; cotton plants Deep ensemble learning Deep learning FFT Transfer learning

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jun 21, 2025

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 Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Agriculture is vital for economic stability, but crop diseases and pests threaten yields.
Early detection and prevention are critical for successful crop management and economic prosperity.

Purpose of the Study:

To develop a framework for detecting healthy and unhealthy cotton plants using advanced computer vision and artificial intelligence.
To enhance the accuracy of disease detection through feature extraction and ensemble learning.

Main Methods:

Utilized deep learning (DL) models including AlexNet, GoogLeNet, InceptionV3, and VGG-19.
Employed feature extraction techniques: Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT).
Implemented an ensemble learning framework to fuse individual model predictions for improved accuracy.

Main Results:

Features extracted using CWT outperformed those from FFT for DL model input.
GoogLeNet achieved 93.4% accuracy with CWT features, closely followed by AlexNet (93.4%) and InceptionV3 (91.8%).
The ensemble learning framework reached 98.4% accuracy using CWT features, outperforming FFT features.

Conclusions:

Scalogram features extracted via CWT enable more accurate plant condition detection using DL models.
Early disease detection in cotton plants leads to improved yields and profitability, positively impacting the economy.