Crop Yield Prediction and Recommendation System

Abstract

Crop yield prediction is a highly composite task influenced by several factors such as state, district, area, rainfall, temperature, soil factors, and many more. For accurate yield prediction, we are required to figure out the functional relationship between the yield and the above-mentioned factors. Comprehensive datasets and powerful algorithms are required to reveal such relationships. This paper is focused mainly on predicting the yield of the crop along with crop recommendations by applying various machine learning techniques. The models going to be used here include Decision Tree and Random Forest. The prediction made by these models will help the farmers to come to a decision on which crop to grow to induce the most yield by considering factors like temperature, rainfall, area, etc. Most of the times farmers consistently grow one crop on one soil for years which reduces the nutrients of the soil, hence a crop recommendation system influenced by the factors such as rainfall, annual temperature, soil content, and type, etc becomes important which saves the soil from getting exhausted while also giving a good return to the farmers.