Harnessing Automated Irrigation Technologies to Enhance Sustainability of Agriculture: A Pathway to Food Security
Prakalya Prabhahar, Sanjay Shriram, Kanaka Shankar, Mohanraj V, Thilagarajan R
International Journal of Environment and Climate Change · pp. 240–252 · Published 3 Dec 2025
10.9734/ijecc/2025/v15i125159Abstract
Agriculture faces the dual challenge of ensuring food security while conserving diminishing water resources amid climate uncertainty. Automated irrigation technologies integrating smart sensors, Internet of Things (IoT) platforms and artificial intelligence (AI) tools offer a transformative solution for precision water management. This study evaluates the impact of automated irrigation on water-use efficiency, energy optimization and crop productivity through field-based data and comparative analyses across diverse systems. Results indicate that high-level automation achieves up to 87% water savings, 30% yield improvement and 2–3 years of return on investment, substantially outperforming traditional irrigation. Labor savings of up to 60% and reduced greenhouse gas emissions further underscore automation’s environmental and economic advantages. Case studies from Tamil Nadu (India), Israel, California and Sub-Saharan Africa validate the global scalability of automation under varied agro-climatic contexts. Despite proven benefits, adoption barriers persist due to high initial investment, limited technical capacity and infrastructural constraints. Policy interventions, including targeted subsidies, institutional linkages and digital literacy initiatives, are essential to promote widespread adoption. Automated irrigation directly supports SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action) by enhancing productivity, conserving water and reducing emissions. When integrated with solar-powered systems, it further strengthens climate-smart agriculture. The findings affirm that automated irrigation represents a paradigm shift toward sustainable, resource-efficient and resilient agricultural systems, enabling a transition from reactive water management to predictive and data-driven farming.
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