Prediction of Crop Yield Based-on Soil Moisture using Machine Learning Algorithms

Abstract

Agriculture planning is playing an important role as the economic growth is very high day by day in our daily life. There is lot of research study going on as there are few important issues like soil nutrients, crop prediction, farming system, crop monitoring in agriculture with modern farming system. Based on various parameters, farming issues and farming system, there is lot of change in production rate and market prices. Crop prediction and crop monitoring is main factor to produce good quality of crops for farmers to predict crop yield based on soil moisture. Prediction of crop yield includes forecasting factors like temperature, humidity, rainfall, etc., and crop yield based on soil moisture includes few measures like pH, NPK (Nitrogen, Phosphorous and potassium) values using various sensors. Farmers can predict or come to a decision the type of soil moisture values, farmers can decide the type of crop to be planted. In this paper, we proposed decision tree supervised machine learning algorithm to improve our results for the prediction of crop yield based on soil moisture parameters to achieve economic growth for achievement of better results.