Mobile application based sustainable irrigation water usage decision support system: an intelligent sensor CLOUD approach

In this paper a novel data integration approach based on three environmental Sensors – Model Networks (including the Bureau of Meteorology-SILO database, Australian Cosmic Ray Sensor Network database (CosmOz), and Australian Water Availability Project (AWAP) database) has been proposed to estimate ground water balance and average water availability. An unsupervised machine learning based clustering technique (Dynamic Linear Discriminant Analysis (D-LDA)) has been applied for extracting knowledge from the large integrated database. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sensor CLOUD computing infrastructure has been used extensively to process big data integration and the machine learning based decision support system. An analytical outcome from the Sensor CLOUD is presented as dynamic web based knowledge recommendation service using JSON file format. An intelligent ANDROID based mobile application has been developed, capable of automatically communicating with the Sensor CLOUD to get the most recent daily irrigation, water requirement for a chosen location and display the status in a user friendly traffic light system. This recommendation could be used directly by the farmers to make the final decision whether to buy extra water for irrigation or not on a particular day.