Abstract
Rural communities face agriculture, resource, and economic challenges worsened by climate change and poor infrastructure. This paper examines AI's role in rural innovation and sustainable systems, proposing a framework with AI tools like predictive crop yield analytics using IoT-sensed data (soil moisture, temperature), remote sensing (drone pest imagery), and satellite sensor data (NDVI vegetation health) with IoT precision farming and ML supply chain optimization for resilience and productivity. Case studies from India, sub-Saharan Africa, and Southeast Asia show low-cost AI via mobile apps and edge computing delivering real-time insights, cutting waste 30% and raising incomes. Participatory design and federated learning tackle data scarcity, divides, and ethics. Findings support UN SDGs, urging policy for scalable tech. AI bridges urban-rural gaps for equitable sustainability.
Keywords
Artificial Intelligence (AI), Rural Innovation, Precision Farming, Sustainable Systems, IoT-Sensed Data, Remote Sensing, Satellite Sensor Data, and Federated Learning.
Citations
IRE Journals:
Tanushree S R, Spoorthi M, Shreya A Hurakadli, Sneha K V, Dr. Arudra A "AI for Rural Innovation and Sustainable Systems" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1007-1012 https://doi.org/10.64388/IREV9I10-1716213
IEEE:
Tanushree S R, Spoorthi M, Shreya A Hurakadli, Sneha K V, Dr. Arudra A
"AI for Rural Innovation and Sustainable Systems" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716213
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