Knowledge mapping trends of Internet of Things (IoT) in plant disease and insect pest study: a visual analysis

The study and literature on the Internet of Things (IoT) and its applications in agriculture for smart farming are increasing worldwide. However, the knowledge mapping trends related to IoT applications in plant disease, pest management, and control are still unclear and rarely reported. The primary...

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Bibliographic Details
Main Authors: Mohd Zawawi, Muhammad Akmal, Jusoh, Mohd Fauzie, Muhammad, Marinah, Naher, Laila, Abdul Latif, Nurul Syaza, Abdul Muttalib, Muhammad Firdaus, Radzuan, Mohd Nazren, Nugroho, Andri Prima
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2023
Online Access:http://psasir.upm.edu.my/id/eprint/102064/1/02%20JST-3618-2022.pdf
http://psasir.upm.edu.my/id/eprint/102064/
http://www.pertanika.upm.edu.my/pjst/browse/prepress-issue?article=JST-3618-2022
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Institution: Universiti Putra Malaysia
Language: English
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Summary:The study and literature on the Internet of Things (IoT) and its applications in agriculture for smart farming are increasing worldwide. However, the knowledge mapping trends related to IoT applications in plant disease, pest management, and control are still unclear and rarely reported. The primary aim of the present study is to identify the current trends and explore hot topics of IoT in plant disease and insect pest research for future research direction. Peer review articles published from Web of Science (WoS) Core Collection (2010-2021) were identified using keywords, and extracted database was analysed scientifically via Microsoft Excel 2019, VOSviewer and R programming software. A total of 231 documents with 5321 cited references authored by 878 scholars showed that the knowledge on the studied area has been growing positively and rapidly for the past ten years. India and China are the most productive countries, comprising more than half (52%) of the total access database on the subject area in WoS. IoT application has been integrated with other knowledge domains, such as machine learning, deep learning, image processing, and artificial intelligence, to produce excellent crop and pest disease monitoring research. This study contributes to the current knowledge of the research topic and suggests possible hot topics for future direction.