Descriptive and predictive analytics of agricultural data using machine learning algorithms

Abstract

Agriculture is one of the domains which should be in the condition of being protected. People are moving towards organic food items. So, attentiveness is needed in agriculture. Rainfall and crop production are the main components of agriculture. Data science is an emerging era in the information world. Data analytics is a part of data science and it provides prolific solutions to the problems through various types of analytical methods such as descriptive, diagnostic, predictive and prescriptive analytics. Machine learning algorithms are helpful in various analytical methods. Data science along with machine learning algorithms will lead to do smart agriculture with good potential outcomes. This chapter deals with analysis of rainfall and crop production in India. Crop production will be predicted based on the rainfall for most of the crops. There are plenty of statistical models that are emerging as machine learning algorithms for many of business circumstances. Time series analysis and regression methods will be applied to forecast the rainfall and crop production in future. Autoregressive integrated moving average (ARIMA) model will be used to do the time series analysis.