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The changes in the climatic conditions are having beneficial as well as harmful effects on crop yields depending on the drastic changes. There can be a yield loss due to the occurrence of disease in crops. Apart from severe yield losses, infected yield can be harmful and threatening to living being’s health as that is the source of food. This also affects the economy of the agricultural depended country. Disease prediction tools advance in the management of exertions for diseases in plants. Machine learning techniques help in elucidating complex associations between hosts and pathogens without invoking difficult-to-satisfy expectations. For the fungal diseases, analysis with multiple regression shows that meteorological parameters comprising of temperature, wind speed, and humidity were the key predictors of fungal attention. The paper discusses the bibliometric analysis of plant disease prediction from the Scopus database in analyzing the research by area, influential authors, institutions, countries, and funding agency. The 490 research documents are extracted from 2015 to 30th December 2020 from the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, ScienceScape, and wordcloud. The visualization aids in a quick and clear understanding of the different perspective as mentioned above in a particular research domain search.
Bioresource and Agricultural Engineering Commons, Computational Engineering Commons, Library and Information Science Commons