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Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis.

Anupam Bonkra1,2, Pramod Kumar Bhatt1, Joanna Rosak-Szyrocka3

  • 1Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur 303002, India.

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Summary
This summary is machine-generated.

This study analyzes artificial intelligence applications for apple leaf disease detection. It maps research trends and identifies key areas for future advancements in agricultural diagnostics.

Keywords:
VOSviewerapple leaves disease detectionbibliographic couplingbibliometricdeep learningmachine learningscientific mapping

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Apple leaf infections cause significant crop losses due to unpredictable weather.
  • Early detection of apple leaf diseases is crucial for preventing yield reduction.
  • Existing research often focuses on specific identification methods rather than a transdisciplinary overview.

Purpose of the Study:

  • To conduct a bibliometric analysis of artificial intelligence in apple leaf disease diagnosis.
  • To map the scientific landscape and identify research trends in this domain.
  • To provide a conceptual framework for future research and practical applications.

Main Methods:

  • Scientometric analysis of 214 documents from 2011-2022 sourced from the Scopus database.
  • Utilized Bibliometrix suite, including Biblioshiny and VOSviewer software.
  • Performed citation analysis, co-citation analysis, and social network analysis.

Main Results:

  • Identified key journals, authors, nations, articles, and research themes in apple leaf disease detection using AI.
  • Revealed the intellectual and social structure of the research field.
  • Synthesized knowledge structures to highlight current trends and research gaps.

Conclusions:

  • The study provides a comprehensive overview of AI's role in diagnosing apple leaf diseases.
  • Offers a conceptual framework and strategic recommendations for future research directions.
  • Highlights the need for transdisciplinary approaches in agricultural disease diagnostics.