AI Driven Agriculture for Nutritional Security and Public Health Improvement - Kumari Sunita; Shalini Singh; Nidhi Singh; Omisha Kannaojiya; Firdaush Jahan; Swati (Public Health, 2026)
Artificial Intelligence (AI) is rapidly transforming agricultural systems by enhancing productivity, efficiency, and sustainability. This review explores the role of AI-driven agriculture in improving nutritional security and public health outcomes globally. With the increasing challenges of climate change, population growth, and resource constraints, conventional agricultural practices are often insufficient to meet rising food demands. AI technologies, including machine learning, remote sensing, and precision farming, provide innovative solutions for optimizing crop production, reducing resource wastage, and improving food quality. AI-based tools enable real-time monitoring of soil health, crop growth, and pest dynamics, allowing farmers to make data-driven decisions that enhance yield and nutritional value. Additionally, AI applications in supply chain management help reduce post-harvest losses and ensure efficient food distribution, thereby improving food accessibility and affordability. These advancements contribute significantly to reducing malnutrition and enhancing dietary diversity, AI-driven agriculture supports public health by minimizing excessive use of agrochemicals, improving environmental sustainability, and reducing exposure to harmful substances. However, challenges such as limited digital literacy, high implementation costs, and data accessibility issues hinder widespread adoption, particularly in developing regions. AI-driven agriculture holds immense potential to strengthen nutritional security and public health. Strategic investments, policy support, and capacity building are essential to harness its full benefits for sustainable development