Modelling Soil Fertilizer Levels and Crop Yields in Agriculture Using Machine Learning

Technology is everywhere in the 21st Century. So, for optimum results in each field, various methods to minimize loss and maximize benefits. The application of Machine Learning in crop type prediction for modern farming is crucial. Also, suggesting the type of fertilizer and amount can increase the application's usability. Various aspects of rain amount and other real-world parameters are taken into account when predicting crop yields and suggesting fertilizer applications. To predict output, the application uses the Flask web framework to store a large number of datasets. The application then creates a link between Random Forest algorithms, Decision Tree algorithms, and XGBoost algorithms. This work focuses on crops that are significant in the area chosen. Based on soil fertility and weather data, the proposed model predicts crop type. The type of fertilizer to use for the chosen crop should be determined. Predict crop yields using a fertilizer calculator and estimate the economic impact of cultivation by calculating the amount of fertilizer needed.