AUTOMATED CROP PREDICTION BASED ON EFFICIENT SOIL NUTRIENTS ESTIMATION USING NAIVE BAYES CLASSIFICATION ALGORITHM

Cereal crops such as rice, wheat, and different pulses account for the majority of India’s food output. Predicting crop yields far ahead of harvest would assist policymakers and farmers in making informed decisions about agronomy, crop selection, and agricultural planning. Such forecasts will also assist related sectors in planning their logistical operations. The goal of the research is to create a machine learning model that can generate such predictions. The model is trained using a dataset that incorporates soil data from the previous decade, with features such as Ph value, temperature, and crop name, as well as labels such as crop yield. The model learns the link between the yield and variables such as soil type, location, and forecast using appropriate machine learning techniques.