Farmers, traders and federal governments in third world countries face many tribulations such as flood, drought, food insecurity, and middleman dominations amongst others. These problems are being addressed by developing nations such as India by creating commodity stock exchanges and research institutions such as the National Institute of Agricultural Marketing (NIAM) and the Indian Council of Agricultural Research (ICAR), and by designing and implementing multiple national agricultural insurance schemes. Success of all the above initiatives is dependent to a great extent on having accurate agricultural intelligent data on time. At times the government takes important decisions on importing wheat, rubber, sugar, etc with insufficient and pseudo geospatial data. Similarly, traders rely on the information provided by the middle men who in turn get this information from the farmers. Today, GIS and remote sensing technologies can play a pivotal role in generating accurate, scientific, and unbiased information on crop locations, production, yield, crop health, cropping patterns, climatic implications and also data for demand forecasting.Through specific project case studies, this paper highlights how remote sensing and GIS technologies can be effectively used to generate accurate agricultural intelligence data for import planning and better supply chain management. Furthermore, the paper also suggests the methodology for import planning and the authenticity of the geospatial data.