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

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. 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 NPK (Nitrogen, Phosphorous and potassium) and pH values using various sensors. Machine learning (ML) is a useful decision-making model for estimating crop yields, and also for deciding what crops to plant and what to do during the crop's growing seasons. To aid agricultural yield prediction studies, a number of analytical techniques have been used. In this study Farmers can predict or come to a decision the type of soil moisture values; Farmers can choose the type of crop they want to sow. In this paper, Author proposed decision tree supervised machine learning algorithm to improve the results for the prediction of crop yield based on soil moisture parameters to achieve better error rate and accuracy for economic growth. It also includes the few machine learning algorithms which are discussed in literature survey, further Author highlighted the proposed system in methodology, and compared the analysis in results to give it a balance view. The future scope is also mentioned to improve it for further studies. This paper will be sufficient for those who are keener in learning about the expectation of crop yield based on soil moisture using ML Algorithms.