Crop Prediction and Fertilizer Recommendation Using Regression Algorithms

Abstract

Abstract: India, a nation with a strong agricultural backbone, relies heavily on the forecast for crop production and agroindustrial products for its economy. The domain of data mining is gaining traction as a valuable tool in the analysis of crop yields. Predicting yields is a crucial aspect of agriculture, as it allows farmers to anticipate their potential harvest. This involves the examination of various relatedfactors such as the pH level, which indicates soil alkalinity. Other important elements include the percentages of essential nutrients like Nitrogen (N), Phosphorus (P), and Potassium (K), as well as the temperature, rainfall, and humidity levels in the region. These data attributes are examined and used to train a range of appropriate machine learning algorithms to create a predictive model. This system aims to provide accurate crop yield predictions and offer users specific recommendations on the type of fertilizer required. The predictions are considering the atmospheric and soil parameters of the territory, with the goal of enhancing crop yield and thereby increasing the farmer’s revenue