Global trend of agricultural precision for plant pathology: Bibliometric study in Scopus database
DOI:
https://doi.org/10.64128/wppj.v1i1.42069Keywords:
Citation metrics, Disease detection, Machine learning, Remote sensing, Sustainable agricultureAbstract
This bibliometric study investigates the global trends in agricultural precision within plant pathology, utilizing data from the Scopus database. This research identifies key research hubs, influential authors, and emerging themes in the domain by analyzing publication patterns, citation metrics, and keyword co-occurrence. The study highlights the increasing integration of advanced technologies such as remote sensing, machine learning, and big data analytics in plant pathology research. The findings underscore the growing emphasis on precision agriculture to enhance disease detection, management, and crop productivity. This comprehensive analysis provides valuable insights for researchers, policymakers, and practitioners aiming to leverage technological advancements for sustainable agricultural practices.
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