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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F23%3A96774" target="_blank" >RIV/60460709:41110/23:96774 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/ijerph20043222" target="_blank" >https://doi.org/10.3390/ijerph20043222</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ijerph20043222" target="_blank" >10.3390/ijerph20043222</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

40101 - Agriculture

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Environmental Research and Public Health

ISSN

1660-4601

e-ISSN

1660-4601

Svazek periodika

20

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

32

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85148963623