DATA MINING ON THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE IN AGRICULTURE

• DOI: https://doi.org/10.22533/at.ed.4002415071

• Palavras-chave: Agricultura. Data Mining. Análise Bibliométrica. Inteligência Artificial Generativa. Dióxido de Carbono. Solos. Sensoriamento Remoto.

• Keywords: Agriculture. Data Mining. Bibliometric Analysis. Generative Adversarial Networks. Carbon Dioxide. Soils. Remote Sensing.

• Abstract: This paper conducts a bibliometric analysis with the purpose of mapping the academic production on the application of Generative Adversarial Networks (GANs) in agriculture. Focusing on articles indexed in the Web of Science and Scopus databases from 2015 to 2023, we explore the growth patterns and profiles of the scientific literature in this area of knowledge. As a basis for the methodology, we used the SSF method, enhanced by the incorporation of a greater number of digital tools. Integrating these tools enabled an improvement in the search process and bias mitigation. The results indicate an approximate 1800 percent increase in the number of publications on GANs in agriculture from 2015 to 2023. The main contributions include the identification of significant trends and potential research gaps in the use of GANs for carbon dioxide optimization in soils with applications of remote sensing from orbital images. We conclude that the increase in articles is due to the growing use of GANs in agriculture. This study not only highlights key areas for future innovation but also demonstrates the importance of bibliometric methods for mapping progress and guiding academic writing.