Next-Generation Precision Farming Integrating AI and IoT in Crop Management Systems

Abstract

Precision farming or precision agriculture, encompassing the use of Information Technology (IT), Geographic Information Systems (GIS), and Remote Sensing (RS), has been instrumental in optimizing field inputs to improve agricultural efficiency. This study aims to explore the evolution of precision farming into next-generation precision farming through the integration of emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). Our research elucidates how these technologies can potentially create a holistic system for crop management, with aspects including real-time monitoring, predictive analytics, automation, precision irrigation, yield optimization, and supply chain integration. For instance, IoT devices can be utilized for real-time data collection about environmental factors, and AI algorithms can process this data for pattern recognition and predictive analytics. AI-powered robotic systems can perform various tasks in the field, reducing manual labor and increasing efficiency. Additionally, precision irrigation and fertilization practices can be optimized using AI and IoT, leading to cost reduction and minimized environmental impact. Machine learning models are highlighted for their ability to predict crop yields and suggest optimization strategies. Moreover, AI can integrate crop production with supply chain logistics and market dynamics, potentially reducing costs and waste. The transformational potential of integrating AI and IoT into precision farming is significant. However, challenges such as data privacy, technology costs, and the digital divide need to be addressed. Thus, the paper underscores the importance of ongoing research, development, and policy-making to fully harness the potential of these technologies in revolutionizing agriculture.