AI-GeoInfo Crop Recommendation Framework Using Decision Tree Classifier and Flask-based GeoAPIs

The crop recommendation AI-GeoInfo Framework is an advanced web application system that assists farmers and agricultural specialists in making informed crop selections based on soil and weather conditions. This framework combines geospatial data, machine learning, and web development technologies to generate tailored crop recommendations. At its core is a Decision Tree Classifier, a model chosen for its reliability and interpretability. The model is trained on comprehensive environmental data, including temperature, humidity, rainfall, soil pH, and composition, with preprocessing handled by the Python SimpleImputer library to address any missing values and enhancement. The web interface is built using Flask-based GeoAPI, which enables users to input geographic coordinates. This identifies the nearest soil mapping unit using GeoPy for precise geodesic distance calculations by linking the input location with relevant soil data. This data is fed into the Decision Tree model, which generates an optimised list of crop recommendations based on the specific conditions of the location. The intuitive interface presents these crop recommendations and uses the AI-GeoInfo framework to provide accurate soil and weather information to its users. The framework also allows easy updates and scalability by adapting diverse agricultural applications and being responsive to advances in data and technology.