USING AI TO OPTIMIZE AGRICULTURAL SUPPLY CHAINS, REDUCING FOOD WASTE AND IMPROVING EFFICIENCY

Abstract

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The agricultural supply chain is a complex network prone to inefficiencies, leading to significant food waste and economic losses. Artificial Intelligence (AI) offers a transformative solution to optimize agricultural supply chains, reducing waste and improving efficiency. This paper explores AI applications in agricultural supply chains, focusing on predictive analytics, route optimization, supply chain visibility, and quality control. AI-powered predictive models forecast demand, detect anomalies, and optimize production planning, reducing food waste by up to 50%. AI-driven route optimization reduces fuel consumption, emissions, and transit times. Real-time tracking and monitoring systems provide visibility, enabling quick response to issues. AI-powered quality control detects defects, monitors quality, and predicts spoilage, ensuring safer food products. Case studies demonstrate AI's potential in optimizing inventory management, irrigation, and supply chain logistics. The paper discusses challenges, future directions, and regulatory frameworks necessary for AI adoption in agriculture, highlighting AI's critical role in ensuring sustainable and efficient agricultural supply chains.