Integration of AI and IoT for Yield Optimization in Precision Farming
Awadhesh Kumar Singh *
Krishi Vigyan Kendra, Pratapgarh, Uttar Pradesh, 229408, India.
Swarnashree Barman
Department of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi, 110012, India.
Aparna P M
Central Public Works Department, India.
Mohammed Hisham M
Department of Agricultural Statistics, College of Agriculture, Kerala Agricultural University, Vellayani, Thiruvananthapuram, Kerla – 695522, India.
Sabarinathan Babu
Department of Remote sensing & GIS, Tamil Nadu Agricultural University, Coimbatore-641003, India.
N. Satheeshkumar
Maize Research Station, Vagarai, Palani, Tamil Nadu Agricultural University, Coimbatore- 641 003, India.
M. Ramasamy
Department of Veterinary and Animal Science, Krishi Vigyan Kendra, Vellore District, Tamil Nadu Agricultural University, Coimbatore- 641 003, India.
Thiruvengadam K
Department of Agricultural Entomology, RVS Agricultural College Thanjavur, Tamil Nadu- 613402, India.
*Author to whom correspondence should be addressed.
Abstract
Precision farming has emerged as a transformative approach to modern agriculture, addressing the challenges of increasing food demand, resource scarcity, and environmental sustainability. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized precision farming by enabling real-time data collection, analysis, and decision-making. This paper explores the role of AI and IoT in optimizing crop yields, reducing resource wastage, and enhancing agricultural sustainability. In this study, the underlying technologies, including IoT sensors, drones, autonomous vehicles, and AI algorithms such as machine learning and deep learning, have been examined. Recent advancements, such as edge computing, digital twins, and blockchain, are discussed, along with their applications in precision irrigation, disease detection, yield prediction, and resource optimization. The study revealed that precise application of water, fertilizers, and pesticides reduces waste and lowers production costs. For instance, AI-powered irrigation systems can reduce water usage by up to 50%, significantly lowering water bills. Automation and data-driven decision-making reduce labor costs and improve operational efficiency. By combining the real-time data collection capabilities of IoT with the advanced analytical power of AI, precision farming enables farmers to make data-driven decisions, optimize resource usage, and maximize crop yields. Moreover, Case Study 1 (Smart Irrigation in California) revealed that the system reduced water usage by 20% while increasing crop yields by 15%. Similarly, Case Study 2 (Disease Detection in India) revealed that the system achieved an accuracy of 90% in disease detection, enabling timely intervention and reducing crop losses. Despite the promising potential, challenges related to data privacy, interoperability, and accessibility remain. Policymakers must create supportive regulatory frameworks that encourage innovation while safeguarding sensitive agricultural data. Looking ahead, emerging technologies such as edge computing, 5G connectivity, blockchain, and advanced AI models promise to further enhance the capabilities of precision farming, making it more accessible, scalable, and efficient. This paper concluded with future prospects, emphasizing the need for continued innovation and collaboration to fully realize the benefits of AI and IoT in precision farming. By addressing these challenges, the integration of AI and IoT can pave the way for a more sustainable and productive agricultural future.
Keywords: Precision farming, artificial intelligence (AI), internet of things (IoT), yield optimization, smart agriculture