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Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction.

Prabhjot Kaur1, Shilpi Harnal1, Rajeev Tiwari2

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.

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Summary

This study introduces an automated method for detecting four grapevine diseases using EfficientNet B7 deep learning and logistic regression. The approach achieves 98.7% accuracy, improving plant disease diagnosis for sustainable agriculture.

Keywords:
EfficientNet B7convolutional neural networkfeature reduction and extractionimage classificationleaf disease detectionplant diseasetransfer learning

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Plant diseases pose significant threats to food security and agricultural economies, necessitating accurate and efficient detection methods.
  • Manual monitoring of grapevine diseases is labor-intensive and often insufficient due to disease diversity and time constraints.
  • Automated plant disease characterization is crucial for sustainable agricultural practices and input management.

Purpose of the Study:

  • To develop and evaluate an automated system for the comprehensive detection of four major grapevine diseases: Leaf blight, Black rot, Stable, and Black measles.
  • To address the limitation of previous studies that focused on detecting only one or two diseases.
  • To investigate the efficacy of deep learning and machine learning techniques for accurate plant disease identification.

Main Methods:

  • Utilized the Plant Village dataset for training and validation.
  • Employed transfer learning to retrain the EfficientNet B7 deep architecture.
  • Applied Logistic Regression for feature down-sampling, followed by state-of-the-art classifiers for final disease identification.

Main Results:

  • The proposed method achieved a high accuracy of 98.7% in detecting the four specified grapevine diseases after 92 epochs.
  • The study successfully identified the most discriminant features for accurate disease classification.
  • A comparative analysis confirmed the effectiveness of the proposed technique against existing methods.

Conclusions:

  • The developed automated system offers a robust and accurate solution for detecting multiple grapevine diseases, surpassing previous research limitations.
  • The combination of EfficientNet B7 and Logistic Regression provides a powerful approach for image-based plant disease diagnosis.
  • The findings suggest a suitable classifier for practical application, contributing to sustainable agriculture through improved disease management.