Artificial Intelligence in Water Management for Sustainable Farming: A Review
Ashoka, P *
Agricultural Reaserch Station, Hanumanmatti(p) Ranebennur(tq), Haveri, Karanataka, India.
B. Rama Devi
Department of Agronomy in KL College of Agriculture, KL University, Vadheswaram, Andhra Pradesh, India.
Nilesh Sharma
Faculty of Agriculture, Jagannath University, Jaipur, India.
Madhumita Behera
Department of Agronomy, Faculty of Agricultural Sciences, Siksha 'O' Anusandhan, Deemed to be University, Bhubaneswar - 751003, Odisha, India.
Abhishek Gautam
Department of Agriculture Engeeneering, AKS University Satna (Madhya Pradesh), India.
Ayushi Jha
Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, Punjab, India.
Gayatri Sinha
Social Science Division, ICAR-National Rice Research Institute, Cuttak, Odisha, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.
Keywords: Irrigation, sustainability, technology, water management, precision, innovation, farming practices
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