Predictive Analytics in Agriculture using Geospatial Mapping

Smart farming has become increasingly popular over the past years and has been making great contributions to the agricultural industry. Techniques such as precision farming, predictive analytics, and geospatial visualization are being used in agriculture to help with efficiency, profitability, and optimization. New Jersey is known as the Garden State for its scenic landscapes and agriculture. Some of its staple field crops include corn, wheat, and soybeans. In particular, this project is focused on analyzing the average amount of soybean yields across the different counties of NJ over the past years to make future predictions. The approach is to use predictive analytics (creating linear regression models and using GIS) on current and past USDA New Jersey soybean yield data. This can then help to discover and analyze future trends. Next, using geospatial mapping (utilizing the ArcGIS platform), the findings drawn from the data will be mapped to provide clarity. These conclusions can be used to provide future direction and make further advancements. For example, an app (that displays the analytics and findings) can be created and translated to the farmers to help provide suggestions on future harvesting and allow them to understand their farms better. In addition, the findings could lead to a further and more detailed study involving AI and satellite imagery of NJ soybean farms/acres.