Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bonkra, Anupam, Bhatt, Pramod Kumar, Rosak-Szyrocka, Joanna, Muduli, Kamalakanta, Pilař, Ladislav, Kaur, Amandeep, Chahal, Nidhi, Rana, Arun Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9961883/
https://www.ncbi.nlm.nih.gov/pubmed/36833921
http://dx.doi.org/10.3390/ijerph20043222
_version_ 1784895865440174080
author Bonkra, Anupam
Bhatt, Pramod Kumar
Rosak-Szyrocka, Joanna
Muduli, Kamalakanta
Pilař, Ladislav
Kaur, Amandeep
Chahal, Nidhi
Rana, Arun Kumar
author_facet Bonkra, Anupam
Bhatt, Pramod Kumar
Rosak-Szyrocka, Joanna
Muduli, Kamalakanta
Pilař, Ladislav
Kaur, Amandeep
Chahal, Nidhi
Rana, Arun Kumar
author_sort Bonkra, Anupam
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9961883
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99618832023-02-26 Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis Bonkra, Anupam Bhatt, Pramod Kumar Rosak-Szyrocka, Joanna Muduli, Kamalakanta Pilař, Ladislav Kaur, Amandeep Chahal, Nidhi Rana, Arun Kumar Int J Environ Res Public Health Article 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. MDPI 2023-02-12 /pmc/articles/PMC9961883/ /pubmed/36833921 http://dx.doi.org/10.3390/ijerph20043222 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bonkra, Anupam
Bhatt, Pramod Kumar
Rosak-Szyrocka, Joanna
Muduli, Kamalakanta
Pilař, Ladislav
Kaur, Amandeep
Chahal, Nidhi
Rana, Arun Kumar
Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis
title Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis
title_full Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis
title_fullStr Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis
title_full_unstemmed Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis
title_short Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis
title_sort apple leave disease detection using collaborative ml/dl and artificial intelligence methods: scientometric analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9961883/
https://www.ncbi.nlm.nih.gov/pubmed/36833921
http://dx.doi.org/10.3390/ijerph20043222
work_keys_str_mv AT bonkraanupam appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT bhattpramodkumar appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT rosakszyrockajoanna appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT mudulikamalakanta appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT pilarladislav appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT kauramandeep appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT chahalnidhi appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis
AT ranaarunkumar appleleavediseasedetectionusingcollaborativemldlandartificialintelligencemethodsscientometricanalysis