AI-Enabled Renewable Energy Systems for Agricultural and Rural Development: Socio-Technical, Economic, and Environmental Perspectives

Abstract

Artificial intelligence (AI) is reshaping renewable energy and agriculture, opening practical pathways to improve efficiency, sustainability, and economic resilience in rural regions. Through machine learning and predictive analytics, AI coordinates generation, storage, and consumption, smoothing variability and reducing waste across distributed energy systems. On farms, AI enables precision irrigation, continuous crop surveillance, and targeted pest control, helping farmers use inputs more judiciously while protecting yields. Economic assessments often foreground barriers, especially high upfront capital and regulatory complexity, yet momentum is building around AI’s capacity to shrink environmental footprints by optimizing water, fertilizers, and energy and by supporting biodiversity-friendly practices. At the same time, responsible deployment must account for AI’s own environmental costs, including energy-intensive computation and hardware lifecycles. Evidence from field deployments already shows returns: energy optimization at microgrids, predictive maintenance that reduces downtime, and autonomous interventions that limit chemical overuse. Looking forward, progress hinges on inclusive, scalable adoption that blends local farming knowledge with digital decision support, ensuring tools reflect diverse climates, crops, and community priorities. Such integration strengthens the robustness and shock tolerance of agri-energy systems under climate and market stress. With careful planning, capacity building, and cross-sector collaboration among farmers, utilities, technologists, and policymakers, AI can substantially advance sustainable rural development, aligning technological innovation with social equity, economic viability, and ecological stewardship. Priorities include open standards, affordable access, and transparent models to build trust and protect privacy, so benefits accrue rather than widening digital divides. With policies and training, AI becomes a practical tool for decisions, not just a high-tech promise.