Crop yield prediction for formers using random forest approach

Abstract

As an agricultural region, India's economy depends primarily on agricultural yield growth and agro-industry goods. Data Mining is an new area of study in crop yield analysis. Prediction of yields is a very critical issue in agriculture. Any farmer is interested in knowing how much yield he's going to make. Analyze various related attributes such as location, pH, etc. Price from which the soil alkalinity is determined. In comparison, the percentage of nutrients such as Nitrogen (N), Phosphorous (P) and Potassium (K) Position is used along with the use of third-party applications such as environment and temperature APIs, soil quality, soil nutrient content in that area, amount of rainfall in the field, soil structure can be calculated. All of these data attributes will be analyzed, or the data will be developed with various correct machine learning algorithms to create a model. The system comes with a model to be accurate and accurate in predicting crop yields and to provide the end user with correct recommendations on the required fertilizer ratio based on the atmospheric and soil parameters of the soil to increase crop yields, and increase farmer revenue.