Related Experiment Video
Updated: Aug 9, 2025
08:20
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
Published on: October 27, 2023
1.5K
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.
International Journal of Environmental Research and Public Health
|February 25, 2023
Summary
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.
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.
