Soil crops and nutrients forecasting using random forest model
Pragya Pranjal, Saahil Mallick, Aniket Paul, Sushruta Mishra, Indu Bhardwaj, Victor Hugo C. de Albuquerque
Abstract
Assessing type of soil and its required nutrients is an important domain in modern agriculture. So moving towards the same vision, this research work addresses this issue of forecasting suitable crops on the basis of environmental factors and its yield based on previous data sets available on the net. The study not only discusses yield and crops, but also stresses on the amounts and types of nutrients present in the soil beforehand by using supervised machine learning algorithms. Among different models used, random forest generates the best performance. Further in the paper we will see how random forest provides us an accuracy of 93% and the least error rate of only 0.3% among all other algorithms using rainfall as a parameter to predict our desired crops.