Optimized Crop Selection Using AI-Driven Predictive Analytics for Enhanced Agricultural Productivity

India's economy is largely dependent on agriculture and people in millions depend on this for their livelihood. Access to new technologies is limited as is small and lad plot tenure, which will reduce production levels. To choose the right crop to cultivate in variety, you have to make efforts to understand the soil, climatic conditions and the environment of a plot in particular. It most often leads to low yield of dict crops, followed by loss of resources and lost money in dict crops. The work prescribes AI driven predictive analytics informed system to assist in crop selection and name the proposed study Optimized Crop Selection using AI driven Predictive Analytics to enhance Agricultural Productivity. The nutrients levels such as Nitrogen, Phosphorus, Potassium as measured from the soil tests as well as the pH of the soil, the temperature, humidity, rainfall and the environmental conditions in the crop selection therefore come from the data obtained from the soil tests. In addition, the recommended results are delivered through the use of machine learning techniques such as Support vector machine (SVM) Artificial neural network (ANN) Random Forest and XGBoost to make sure that the final results are practical and true. It employs a boosting learning method based on various models to accomplish a better prediction than those in separate. Moreover, these models are trained and validated on different aspects of soil testing data and hence applicable for any region in India. The application of technology in farming provides the output with stronger potential to increase, handles and minimizes burdens with minimal costs.