Use of AI in Agricultural Supply Chain: A Case of Gujarat Warehouse Owners and Bhusari Cold Storage

India’s agricultural sector faces myriad challenges, including inefficiencies in supply chains, waste management, and economic disparities among farmers. Integrating Artificial Intelligence (AI) presents a promising solution to these challenges. The paper attempts to find out how AI technologies can be leveraged to enhance the agricultural supply chain in India, focusing on predictive analytics, supply and demand forecasting, quality control, logistics, precision agriculture, market connectivity, smart farming equipment, and risk management. The application of AI in predictive analytics can profoundly impact crop management by utilizing data from diverse sources such as weather conditions, soil quality, and plant health. By analyzing this data, AI models can predict optimal planting times, potential pest attacks, and forecast yields, which are crucial for maximizing output and efficiency in agricultural practices (Smith et al., 2021). AI can significantly enhance the prediction of market demands, which helps in aligning agricultural production with market needs. This reduces the risk of surplus and deficit, optimizing the economic returns for farmers. AI models can analyze historical data patterns and other economic indicators to forecast demand more accurately than traditional methods (Jones & Silva, 2019). Through machine learning and image recognition technologies, AI can automate the process of quality control throughout the agricultural supply chain. This technology enables early detection of plant diseases and pests, ensuring that only high-quality produce reaches the consumer. This not only helps in reducing waste but also in maintaining the health of crops (Lee et al., 2020). AI-driven logistics can revolutionize the transport of agricultural produce from farms to markets by optimizing delivery routes and schedules based on real-time data such as traffic patterns and weather conditions. This optimization helps reduce delivery times and costs, thus minimizing spoilage and enhancing profitability (Kumar & Singh, 2018). Precision agriculture involves the application of AI to ensure precise application of water, fertilizers, and pesticides, tailored to the specific requirements of each crop segment. This targeted approach helps in reducing resource wastage and environmental impact while enhancing crop productivity (Patel & Shah, 2020). Market Connectivity, AI can facilitate platforms that connect farmers directly with retailers and consumers, which minimizes the dependency on multiple intermediaries. This direct connection can lead to better pricing for farmers and fresher products for consumers, thus improving economic outcomes for agricultural stakeholders (Chen et al., 2019).