Emerging AIoT Technologies for Efficient Data Collection and Decision Making in Smart Farming

Abstract

Traditional farming is being transformed into intelligent, data-driven agriculture by the confluence of Artificial Intelligence (AI) and the Internet of Things (IoT) into the AIoT paradigm. In order to increase agricultural output, optimize resource use, lessen environmental effects, and improve farmer decision-making, smart farming uses real-time data from distributed sensors, automated systems, and predictive models. The growing use of AIoT technology in agriculture addresses global issues such as resource limitations, population expansion, labor problems, and climate change. Wireless sensor networks (WSNs), drones and autonomous vehicles, edge computing, cloud analytics, and machine learning algorithms for predictive insights are key elements of AIoT systems in agriculture. In order to initiate automatic activities or offer decision assistance, these systems gather diverse data, including soil moisture, weather, crop health indicators, and equipment status. These data are then processed and analyzed. In order to improve data collection and decision-making in smart farming, this article examines new AIoT technologies. We look at the integration of various technologies, their advantages, real-world applications, issues with connectivity, security, data quality, and farmer adoption, as well as potential avenues for future research. This study uses an interdisciplinary approach to identify trends, gaps in existing practice, and tactics to optimize AIoT's influence in sustainable agriculture.