Artificial Intelligence-Driven Data Innovation for Environmental Sustainability in India: Smart Agriculture, Water Resource Management, and Urban Air Quality Monitoring

DOI

https://www.doi.org/10.59256/indjcst.20260502040

Published In

Volume 5, Issue 2 (2026)

Published Online

May-August 2026

Pages

366-371

Article Type

Original Article

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India feeds 1.4 billion people using an agricultural system that wastes roughly 40 percent of its irrigation water, loses 30 percent of harvested grain to post-harvest spoilage, and operates largely on the accumulated intuition of farmers whose grandfathers farmed the same land with essentially the same methods. Meanwhile, fourteen of the world’s twenty most polluted cities are Indian, the Ganges carries industrial effluent alongside religious offerings, and groundwater tables across the Indo-Gangetic Plain are dropping at rates that terrify anyone who bothers to look at the data. This paper investigates how artificial intelligence and data-driven innovation are being deployed and could be further deployed to address India’s converging environmental crises. Through field investigation across eleven AI-enabled environmental projects spanning seven Indian states, analysis of operational data from three large-scale smart agriculture platforms serving over 340,000 farmers, and semi-structured interviews with fifty-three professionals in agricultural technology, water resource engineering, air quality science, and environmental policy, we document both genuine achievements and uncomfortable limitations. AI-optimized irrigation advisory systems reduce water consumption by 22–38 percent in pilot deployments. Machine learning air quality forecasting achieves 84 percent accuracy for 48-hour PM2.5 predictions in Delhi’s complex atmospheric environment. Satellite-based crop health monitoring covers 12 million hectares across six states. Yet adoption remains concentrated among larger, better-resourced farmers, data infrastructure gaps limit model accuracy in precisely the region’s most environmentally stressed, and the institutional capacity to translate AI insights into policy action remains underdeveloped.