Sustainable Development Using IoT and AI Techniques for Water Utilization in Agriculture

Introduction

Water is crucial for agriculture, providing essential nutrients and sustaining food production. However, water scarcity and deteriorating quality are interconnected challenges affecting crop yields, food security, and environmental sustainability. Inefficient water management practices and wasteful utilization exacerbate these issues. To address these issues and promote sustainable agricultural development, the integration of IoT and AI techniques in water treatment and utilization is gaining attention as a transformative approach. Agriculture accounts for a substantial portion of the world's freshwater consumption, representing approximately 70% of global water withdrawals. As the global population is projected to reach 9 billion by 2050, the demand for food and water is expected to increase significantly (Karthik et al., 2023; Koshariya, Kalaiyarasi, et al., 2023; Maguluri et al., 2023; Syamala et al., 2023). Concurrently, climate change-induced uncertainties, such as irregular precipitation patterns and more frequent extreme weather events, are further straining water availability. To safeguard food production and mitigate the impact of water scarcity, innovative and sustainable solutions are urgently needed.

Traditional agricultural practices often rely on conventional irrigation methods, such as flood irrigation or overhead sprinklers, which result in significant water wastage and uneven distribution across fields. Moreover, the indiscriminate use of fertilizers and pesticides contaminates water sources, compromising water quality and posing risks to human health and ecosystems. To address these issues, a paradigm shift is required in water management practices, emphasizing resource efficiency, precision irrigation, and conservation-oriented practices. The integration of IoT and AI technologies offers a promising avenue to revolutionize water management in agriculture (Boopathi, 2023c; Hema et al., 2023). IoT devices, including sensors and connected devices, enable real-time data collection and monitoring of various environmental parameters, such as soil moisture levels, weather conditions, and water quality. These devices create a networked ecosystem that facilitates the continuous exchange of information between agricultural systems and decision-makers. By providing accurate and timely data, IoT devices enhance the understanding of water dynamics and enable informed decision-making (Reddy et al., 2023; Samikannu et al., 2022).

AI, on the other hand, empowers agricultural systems with the ability to analyze vast datasets and extract meaningful insights. Machine learning algorithms can process historical and real-time data to predict water demand, optimize irrigation schedules, and identify anomalies, leading to more efficient water usage (Anitha et al., 2023; Boopathi, Sureshkumar, et al., 2022; Vanitha et al., 2023). AI-driven precision irrigation systems tailor water application to the specific needs of crops and soil conditions, reducing water wastage and increasing crop productivity. Additionally, AI techniques can assist in water quality monitoring, enabling early detection of contamination and timely remediation measures. One of the primary applications of IoT and AI integration in agriculture is smart irrigation. Smart irrigation systems utilize IoT sensors to monitor soil moisture levels, temperature, humidity, and other relevant parameters (Bikash Chandra Saha, 2022; Boopathi, Lewise, Sarojwal, et al., 2022; Jeevanantham et al., 2022). This data is then analyzed using AI algorithms to determine the precise amount of water required by each crop, ensuring that water is delivered in optimal quantities and at the right time. This targeted approach to irrigation minimizes water wastage, reduces energy consumption, and fosters sustainable water use practices.