Linking Artificial Intelligence and Insect Genetics for Coffee Plantation Research: A Bibliometric Perspective - Priyambodo, Priyambodo; Parabi, M. Iqbal; Rustiati, Elly Lestari; Permatasari, Nindy; Amrullah, Syarif Hidayat; 'Aliyah, Siti Hamidatul (Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia, 2025)
Artificial intelligence (AI) has advanced rapidly over the past two decades, creating opportunities to address ecological and agricultural challenges by integrating computational methods with genetics. This study aims to map the scientific landscape of research at the intersection of AI, insect population genetics, and coffee agroecosystems. A bibliometric analysis was conducted using the Scopus database, covering publications from 1978 to 2025 and employing co-occurrence, co-authorship, centrality, and co-citation analyses supported by VOSviewer for visualization. The results show a significant growth in publications since 2010, with the United States and Brazil as leading contributors, while collaborations among influential authors and institutions have shaped three main clusters: ecology and agroecosystem management, insect–pest interactions and biological control, and genetics with molecular approaches to plant metabolism. The co-citation network further highlights the integration of pest ecology, biodiversity conservation, and the economic value of pollinators as central themes. These findings indicate that AI–genetics integration is increasingly pivotal for sustainable coffee management, with future research directions emphasizing predictive modeling of pest and pollinator dynamics under climate variability, alongside investigations into soil microbiomes and pollinator health to enhance resilience in coffee production systems.