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Automating Citrus Budwood Processing for Downstream Pathogen Detection Through Instrument Engineering
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Classification of Citrus Diseases Using Optimization Deep Learning Approach.

Ahmed Elaraby1, Walid Hamdy2, Saad Alanazi3

  • 1Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt.

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Summary
This summary is machine-generated.

This study introduces a deep learning approach for automated citrus plant disease detection and classification. The system achieved 94% accuracy, offering a faster and more reliable alternative to manual diagnosis by plant pathologists.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Manual diagnosis of citrus plant diseases by experts is time-consuming and subjective.
  • Citrus diseases significantly reduce crop productivity and economic returns.
  • Computer vision and image processing offer potential for automated disease detection.

Purpose of the Study:

  • To develop and evaluate a deep learning-based system for detecting and classifying citrus plant diseases.
  • To improve the efficiency and accuracy of citrus disease diagnosis.
  • To mitigate financial losses caused by citrus plant diseases.

Main Methods:

  • Utilized deep learning models, specifically AlexNet and VGG19, for image analysis.
  • Employed image processing techniques for feature extraction and classification.
  • Evaluated the approach on established datasets, including the citrus disease image gallery and combined datasets.

Main Results:

  • The system successfully identified and classified various citrus diseases like anthracnose, black spot, canker, scab, greening, and melanose.
  • Achieved a peak performance accuracy of 94%.
  • Demonstrated superior performance compared to existing disease detection methods.

Conclusions:

  • The proposed deep learning approach provides an effective and efficient method for citrus plant disease diagnosis.
  • Automated systems can significantly aid plant pathologists in disease identification.
  • This technology has the potential to reduce economic losses in citrus cultivation.