Trends in agricultural technology: a review of US patents

Abstract Background In recent decades, technological advances have significantly transformed various sectors of the economy, including agriculture. Precision agriculture, as a multidisciplinary and integrative approach, relies on the convergence of diverse technologies to optimize decision-making and enhance productivity, efficiency, and sustainability. In these evolving and complex technological landscapes, patents play a pivotal role, both as a precursor to technological advances and as a critical infrastructure for protecting and advancing innovation progress. The aim of this review is to analyze the evolution of all agricultural technologies, with a focus on precision agriculture, and to compare the position and evolution of each category over time. Methods We review US patents granted from 2014 to 2024 to identify trends in agricultural technology, with a particular focus on their relevance to precision agriculture. Using a framework adapted from Moreno et al. (2024), we classify patents into eight agricultural technology categories: Automation, Control & Robotics; Bioengineering & Biotechnology; Computing & Cloud Technology; Data Acquisition & Communication; Data Science & Artificial Intelligence; Information Systems; Manufacturing Technologies & Equipment; and Resource-related Technologies. Results The prevailing categories were Biotechnology & Bioengineering, Manufacturing Technologies & Equipment, and Automation, Control & Robotics. The first two outrank Automation, Control & Robotics in terms of number of patents, but their number of patents declined in the last year, while Automation, Control & Robotics shows a slight but steady growth. A relatively low frequency of patent submission in Data Acquisition & Communication, Data Science & Artificial Intelligence, Computing & Cloud Technology, and Information Systems, underscores the untapped potential for digital transformation in precision agriculture. These technologies are essential for real-time monitoring, advanced data analytics and decision support systems, and are fundamental to precision agriculture. However, it is worth noting that Data Science & Artificial Intelligence has experienced steady growth over the past four years. Conclusions The interrelated nature of these technology categories highlights the need for an integrated approach to innovation, where advances in one area can synergistically enhance others. This study provides valuable insights for policy makers, researchers and industry stakeholders, and highlights the critical role of precision agriculture in addressing global agricultural challenges.