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

Anupam Bonkra, Pramod Kumar Bhatt, Joanna Rosak-Szyrocka (), Kamalakanta Muduli (), Ladislav Pilař, Amandeep Kaur, Nidhi Chahal and Arun Kumar Rana
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Anupam Bonkra: Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur 303002, India
Pramod Kumar Bhatt: Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur 303002, India
Joanna Rosak-Szyrocka: Department of Production Engineering and Safety, Faculty of Management, Częstochowa University of Technology, 42-200 Częstochowa, Poland
Kamalakanta Muduli: Department of Mechanical Engineering, Papua New Guinea University of Technology, Lae 411, Morobe, Papua New Guinea
Ladislav Pilař: Department of Management, Faculty of Economics and Management, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
Amandeep Kaur: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140417, Punjab, India
Nidhi Chahal: Chandigarh Engineering College, Chandigarh Group of Colleges, Landran, Mohali 140307, Punjab, India
Arun Kumar Rana: Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida 203201, India

IJERPH, 2023, vol. 20, issue 4, 1-32

Abstract: Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011–2022. In order to conduct the study, the Bibliometrix suite’s VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

Keywords: apple leaves disease detection; deep learning; machine learning; bibliometric; scientific mapping; bibliographic coupling; VOSviewer (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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