An IoT-Based Predictive Analytics for Estimation of Rainfall for Irrigation

Abstract

Agribusiness is the foundation of Indian Economy. Its prosperity depends overwhelmingly on the climatic parameters. Occasions like erratic atmosphere are outside human capacity to control. Water assumes a noteworthy element in the development of a product (crop). If there is lacking water supply, chances of product disaster are more. Agriculturists fall into commitments since they have to go up against an uncommon yield’s effectiveness in view of deficient water supply and other atmospheric conditions which increase the peril of their benefit and the high expense of living. Along these lines, it winds up critical to anticipate the measure of rainfall utilized for irrigation. This will guarantee the budgetary use of water. This research paper presents a study and experimentation of predictive analytics to predict the amount of rainfall for irrigation. Predictive analytics include the extraction procedure of valuable data from the informational collection given by the client and foresee critical highlights or patterns. Prediction process is performed using supervised machine learning techniques. Multiple linear regression, k-nearest neighbour, decision tree, and random forest techniques are considered for building the predictive model, where these models are evaluated using root mean squared error. Root mean squared error obtained for multiple linear regression, k-nearest neighbour, decision tree, and random forest are 0.165, 0.103, 0.094, 0.083, respectively. Evidently, random forest shows less root mean squared error compared to other models and is considered for the prediction process. Also, an IoT-based weather station has been built to retrieve the real-time data from a sample area.