Systematic Literature Review of Generative AI and IoT as Key Technologies for Precision Agriculture

Abstract

This study examines the convergence of Generative Artificial Intelligence (AI) and the Internet of Things (IoT) as key drivers of innovation in Precision Agriculture. It posits that these technologies enable real-time monitoring of critical variables such as soil moisture, temperature, and crop health, as well as early detection of pests and diseases. The main objective, through a systematic review of 74 papers, is to identify the applications, benefits, and challenges of Generative AI and IoT. The Kitchenham (2004) methodology was applied along with the PRISMA flow, ensuring transparency and replicability. Five research questions were formulated focusing on crop types, IoT devices, thematic topics, conceptual evolution, keywords, and international collaboration. Searches were conducted across five databases. From an initial pool of 39,223 references and after applying exclusion criteria, 74 papers were selected for analysis. The findings confirm that Generative AI and IoT have reached a level of maturity in intensive crops and high-value sectors, supported by low-cost architectures and advanced data analytics. However, gaps remain, such as the lack of economic assessments of hybrid platforms and the scarcity of public datasets that hinder the replication of certain studies. This study offers practical and strategic guidance to support the implementation of Generative AI and IoT in precision agriculture.