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Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic.

Nouman Butt1, Muhammad Munwar Iqbal1, Shabana Ramzan2

  • 1Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan.

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Summary

This study introduces an automated system for citrus disease classification using deep learning, achieving 99.6% accuracy. This AI-driven approach enhances disease detection in citrus farming for improved crop yield and sustainability.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Citrus farming is vital to Pakistan's economy, contributing 30% of fruit production, primarily in Punjab.
  • Citrus crops face significant disease threats (canker, scab, black spot), reducing fruit quality and yield.
  • Manual disease diagnosis is slow, inaccurate, costly, and requires expert knowledge.

Purpose of the Study:

  • To develop an automated disease classification system for citrus crops.
  • To enhance diagnostic accuracy, efficiency, and cost-effectiveness in disease detection.
  • To provide a scalable solution for sustainable citrus farming practices.

Main Methods:

  • Implementation of a deep learning framework with optimal feature selection.
  • Utilized data augmentation and transfer learning techniques.
  • Employed pre-trained models including DenseNet-201 and AlexNet for classification.

Main Results:

  • Achieved a classification accuracy of 99.6% on a citrus leaves dataset.
  • The proposed automated system demonstrated superior performance compared to existing methods.
  • The framework offers a robust and efficient solution for citrus disease identification.

Conclusions:

  • The developed deep learning system provides an accurate and efficient method for citrus disease classification.
  • This technology supports sustainable agriculture by improving disease management in citrus farming.
  • The automated system addresses the limitations of traditional manual diagnosis, offering a scalable solution.