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Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Jayme Garcia Arnal Barbedo1

  • 1Embrapa Agricultural Informatics, Campinas, SP Brazil.

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This survey reviews digital image processing methods for detecting, quantifying, and classifying plant diseases using visible leaf and stem symptoms. It categorizes techniques to aid researchers in plant pathology and pattern recognition.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Pathology

Background:

  • Plant diseases pose a significant threat to global food security.
  • Accurate and early detection of plant diseases is crucial for effective management.
  • Digital image processing offers a non-destructive approach to disease diagnosis.

Purpose of the Study:

  • To provide a comprehensive survey of digital image processing techniques for plant disease analysis.
  • To categorize existing methods based on their objectives: detection, quantification, and classification.
  • To offer an accessible overview for researchers in plant pathology and pattern recognition.

Main Methods:

  • The study surveys methods utilizing digital image processing in the visible spectrum.
  • Focus is placed on techniques analyzing visible symptoms in plant leaves and stems.
  • Selected methods are classified by their primary objective (detection, quantification, classification) and technical approach.

Main Results:

  • The survey categorizes plant disease analysis methods into detection, severity quantification, and classification.
  • Sub-classification is based on the main technical solutions employed within each category.
  • The review consolidates diverse approaches for analyzing visible plant disease symptoms.

Conclusions:

  • Digital image processing offers robust tools for plant disease detection, quantification, and classification.
  • This survey provides a structured overview of current methodologies.
  • The findings are valuable for advancing research in automated plant disease diagnosis and management.