Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

Abstract

The study’s goals were to examine the effects of unforeseen weather circumstances on agricultural productivity and to test artificial intelligence-based strategies for feature extraction and segmentation for plant leaf disease identification. A bibliometric analysis was used in the study to look into current trends, publications, citation structures, patterns of ownership and collaboration, production patterns, and other aspects connected to the early detection of apple diseases. While there have been many studies on recognising apple illnesses, there have been few attempts to develop a thorough multidisciplinary science map because disease detection is not exclusive to any one field of study. The study’s objectives were to combine knowledge frameworks and identify the current state of the research field. A scientometric examination of 200 papers from the Scopus database between 2017 and 2022 was done by the researchers in order to accomplish this goal. VOSviewer was utilised for the research, and its automated workflow was used to pick significant journals, authors, nations, articles, and themes. To find patterns and trends, social network studies, citation and co-citation checks, and citation counts were all carried out. The study revealed changes in the cognitive and social structure of the area and concluded that the interdisciplinary field of sickness detection employing segmentation has expanded over time. The study also revealed areas that need additional investigation and clarified the intellectual underpinnings of the topic.