Prediction of Soil Fertility Using ML Algorithms and Fertilizer Recommendation System

Prediction of soil nutrient is one of the important primary input management systems to increase crop yield. The nutrient present in the soil plays a major role in the healthy growth of the plants. There are many nutrients present in the soil such as Nitrogen (N), Phosphorus (P), Potassium (K) Calcium (Ca), Magnesium (mg) and Sulphur (S), etc. Farmers do not have sufficient knowledge and information about the nutrient present in the soil. Therefore, they apply a lot of fertilizers in the soil, which causes the unbalanced soil nutrition levels. Hence, the aim this work is to predict the available nutrients in the soil using various Machine Learning (ML) techniques. In this work, three macro nutrients namely NPK which are needed for healthy plant growth are focused. Classification algorithms were used for prediction of soil fertility. Among all of these algorithms, Random Forest give the highest accuracy of 84%. With the results obtained, the model is deployed using web app for recommending fertilizer which is very helpful to the farmers.