Precision Agricultural Studies in the United States of America: A Systematic Map

Despite the United States’ leadership in agricultural innovation, systematic mapping of precision agriculture (PA) research, particularly integrating livestock monitoring, remains limited. This study aims to comprehensively map the spatial and thematic distribution of PA research across the U.S. from 2012 to 2022, identify key focus areas, and highlight critical research gaps to inform future priorities. A systematic literature review was conducted using major scientific databases (EBSCO Host and Google Scholar), initially retrieving 25,568 publications. After applying rigorous inclusion and exclusion criteria, 239 peer-reviewed articles were selected for analysis. Studies were coded by focus area (e.g., sensor/GIS/remote sensing, livestock monitoring, IoT, AI) using a standardized coding framework. The screening process involved dual independent reviewers to ensure reliability. Data visualization and spatial analysis were performed using ArcGIS Pro, enabling the creation of a detailed systematic PA research map of the USA. The spatial mapping revealed that California (n = 80), Texas (n = 53), and Florida (n = 49) accounted for the majority of PA research output. Thematic analysis showed that 29.7% of studies focused on sensor, GIS, and remote sensing technologies, while only 1.5% addressed livestock monitoring. Research on emerging technologies such as IoT and AI was also notably sparse. These findings indicate significant regional and thematic disparities, with livestock monitoring and advanced digital technologies underrepresented in current U.S. PA research. This systematic mapping highlights a critical need to expand PA research beyond crop-focused applications, particularly in livestock monitoring and the adoption of IoT and AI technologies. To address geographic and thematic imbalances, targeted funding and support from agencies such as the USDA are recommended, especially for underrepresented states. Future research should prioritize the integration of real-time IoT livestock monitoring and AI-driven crop yield prediction, particularly in climate-sensitive regions, to ensure sustainable and equitable advancements in U.S. agriculture.