This chapter explores the integration of AI techniques in water resource management for soil-based and soilless irrigation systems in agriculture. It emphasizes the importance of AI in optimizing water use and data-driven decision-making. AI-powered techniques like machine learning and predictive analytics enable precise water optimization. Soilless systems like hydroponics, aquaponics, and aeroponics also contribute to water efficiency. AI is crucial in weather forecasting, climate adaptation, crop water estimation, and water use optimization in water-stressed environments. The chapter discusses successful AI implementations, cost-benefit analysis, ethical, social, environmental considerations, equity, access, and sustainability. Future prospects, advancements, and challenges in AI techniques for water management are explored, emphasizing the need for AI adoption in water-efficient farming practices.
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Water resource management is crucial for sustainable agriculture, especially in the face of water scarcity and global population demands. Efficient water use is essential for long-term viability and environmental conservation. AI advancements have revolutionized water management strategies in agriculture, integrating AI techniques in soil-based and soilless irrigation systems. This integration leads to informed decisions, improved water efficiency, higher crop yields, and reduced environmental impact. The text discusses water resource management in agriculture, emphasizing its importance for food security and sustainable development. It highlights challenges faced by conventional irrigation methods, such as inefficiencies, over-irrigation, and water wastage. AI techniques are crucial for optimizing water use, as they process vast amounts of data, recognize patterns, and make accurate predictions. Integrating AI in soil and soilless irrigation systems could revolutionize traditional farming practices and create a water-smart agriculture era(Vanitha et al., 2023).
This chapter explores AI applications like machine learning, data analytics, predictive modeling, and real-time monitoring for monitoring soil moisture levels, crop water requirements, and adapting irrigation strategies. It also discusses the use of AI in soilless systems like hydroponics, aquaponics, and aeroponics for improved crop growth. However, it also addresses challenges and limitations, including ethical considerations, equitable access to AI-driven irrigation technologies, and environmental sustainability. This chapter explores AI techniques in water resource management for soil and soilless irrigation systems, aiming to inspire agricultural stakeholders to adopt water-efficient and sustainable farming practices. AI integration can contribute to global food security, address challenges like water scarcity, climate change, and population growth(Srivastava et al., 2022).
This review explores the use of AI techniques like machine learning and data analytics in precision irrigation. It emphasizes the optimization of irrigation scheduling, monitoring soil moisture, and predicting crop water requirements, resulting in improved water use efficiency. This review explores the use of AI techniques like machine learning and data analytics in precision irrigation. It emphasizes the optimization of irrigation scheduling, monitoring soil moisture, and predicting crop water requirements, resulting in improved water use efficiency. The research paper explores AI implementation in agricultural water management, focusing on challenges and opportunities. It discusses climate adaptation strategies, smart irrigation controllers, and data analytics for addressing water scarcity and enhancing reuse practices(Malik et al., 2021).
This systematic literature review explores the applications of AI in agriculture, including water resource management. It highlights AI techniques in irrigation systems, including soil moisture sensing, data-driven decision-making, and adaptive irrigation approaches. This review paper discusses machine learning techniques for smart water management in precision agriculture, focusing on AI's potential to optimize irrigation schedules, predict crop water needs, and improve water recycling and reuse practices(Afzaal et al., 2023).
This review explores AI's role in sustainable agriculture, focusing on water resource management. It examines applications in hydroponics, aquaponics, and aeroponics, focusing on optimizing water and nutrient delivery. AI-driven decision-making and real-time soil moisture sensing result in water-efficient irrigation practices and improved crop yields. This research paper presents an AI-based optimization approach for optimal water allocation in agricultural river basins. It uses genetic algorithms and other techniques to achieve optimal distribution for irrigation, considering constraints and stakeholders' needs(Kalyani & Collier, 2021).