Enhancing agricultural supply chain efficiency through artificial intelligence

The global agricultural sector faces mounting pressure to feed a growing population while minimizing waste and environmental impact. This study examines how artificial intelligence technologies can address critical inefficiencies in agricultural supply chains. Through systematic analysis of AI applications in logistics, waste reduction, and distribution, we explore machine learning algorithms, predictive analytics, and computer vision systems that optimize farm-to-consumer pathways. Our findings demonstrate that AI-driven demand forecasting reduces inventory costs by 15-25%, while computer vision systems cut post-harvest losses by up to 30%. However, implementation barriers including high costs, technical expertise gaps, and infrastructure limitations remain significant. The research reveals that successful AI integration requires strategic planning, adequate investment, and supportive policy frameworks. These insights contribute to understanding how emerging technologies can transform agricultural supply chains while highlighting practical considerations for stakeholders.