Efficient Use Crop Yield Prediction Forecasting And Fertilizer Recommendation

India being an agriculture country, its economy predominantly depends on agriculture yield growth and agro industry products. Any farmer is interested in knowing how much yield he is about to expect. Along with it, percentage of nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K) Location is used along with the use of third-party applications like APIs for weather and temperature, type of soil, nutrient value of the soil in that region, amount of rainfall in the region, soil composition can be determined. All these attributes of data will be analyzed train the data with various suitable machine learning algorithms for creating a model. The system comes with a model to be precise and accurate in predicting crop yield and deliver the end user with proper recommendations about required fertilizer ratio based on atmospheric and soil parameters of the land which enhance to increase the crop yield and increase farmer revenue. Keeping accurate data is an important aspect of agricultural risk management. Propose to use machine-learning techniques to develop a prediction model for crop yield production.