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Related Experiment Video

Updated: Jun 28, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Deep transfer learning with gravitational search algorithm for enhanced plant disease classification.

Mehdhar S A M Al-Gaashani1, Nagwan Abdel Samee2, Reem Alkanhel2

  • 1School of Resources and Environment, University of Electronic Science and Technology of China, 4 1st Ring Rd East 2 Section, Chenghua District, Chengdu, 610056, Sichuan, China.

Heliyon
|April 11, 2024
PubMed
Summary

This study introduces a new method for early plant disease identification using transfer learning and Gravitational Search Algorithm (GSA) optimization, achieving 99.2% precision. The approach significantly reduces features, enhancing efficiency for global food security.

Keywords:
Convolutional neural networkDeep learningGravitational search algorithmPlant diseaseTransfer learning

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Plant diseases pose a significant threat to global food security, causing substantial crop damage and economic losses.
  • Early and accurate identification of plant diseases is crucial for effective management and mitigation strategies.

Purpose of the Study:

  • To develop and evaluate a novel computational method for the early identification and classification of plant diseases.
  • To enhance the efficiency and accuracy of plant disease diagnosis through advanced feature extraction and optimization techniques.

Main Methods:

  • Utilized transfer learning with pretrained models (MobileNetV2, ResNe50V2) for multilayer feature extraction from plant leaf images.
  • Employed the Gravitational Search Algorithm (GSA) for optimizing extracted features, followed by Multinomial Logistic Regression (MLR) for classification.
  • Compared GSA optimization with Genetic Algorithm (GA) and contrasted MLR with K-Nearest Neighbors (KNN) for performance evaluation.

Main Results:

  • The proposed GSA-optimized model achieved high classification precision, with an average of 99.2% for MLR and 98.6% for KNN.
  • Demonstrated a significant reduction in feature count by over 50% without compromising diagnostic accuracy.
  • Outperformed models using GA-optimized features, confirming the superiority of the GSA-based approach.

Conclusions:

  • The developed method offers a robust and efficient solution for early plant disease detection, integrating sophisticated computational techniques into agriculture.
  • This data-driven approach enhances plant health management strategies, contributing to improved worldwide food security.
  • The significant feature reduction highlights the method's efficiency, reducing processing demands for practical agricultural applications.