Bibliometric Analysis of Plant Disease
DOI:
https://doi.org/10.58812/wsa.v4i03.3089Keywords:
Bibliometric Analysis, Plant Disease, Plant Pathology, Disease Detection, Artificial IntelligenceAbstract
Plant diseases pose a severe problem in terms of agricultural production, global food security, and sustainable agriculture. The current research seeks to understand the development, research trends, intellectual structure, and new topics of plant disease studies via the bibliometric analysis method. The papers devoted to plant disease research have been studied using several bibliometric techniques such as citation analysis, co-authorship analysis, keyword co-occurrence analysis, thematic evolution analysis, and density visualization. It appears that plant disease studies have greatly grown in size and become a multidisciplinary field covering plant pathology, microbiology, molecular biology, biological control, and agricultural engineering. The highly cited papers show the relevance of the research in such areas as plant-pathogen interactions, mechanisms of disease resistance, sustainable disease management, and artificial intelligence applications to disease detection. According to the results of the keyword analysis, the research is being changed from a biological to a technological one with deep learning, machine learning, image processing, and automated disease diagnosis gaining their popularity. The analysis of cooperation reveals that China, India, and the USA are playing the leading roles in plant disease research in the global scientific network. The current research gives the understanding of the evolution of plant disease research and shows the future directions of plant disease studies with an emphasis on the application of artificial intelligence and molecular approaches.
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