A Machine Learning Based Decision Support System for Improvement of Smart Watering Equipment in Agricultural Fields
Sreenivasulu Vasu1, Vikram Neerugatti2, C. Naga Swaroopa3
1Sreenivasulu Vasu, Assistant Professor, Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Chittoor (Andhra Pradesh), India.
2Vikram Neerugatti, Research Scholar, Department of Computer Science and Engineering, SVUCE, Sri Venkateswara University, Tirupati (Andhra Pradesh), India.
3C. Naga Swaroopa, Assistant Professor, Department of Computer Science and Engineering, Annamacharya Institute of Technology and Science, Tirupati (Andhra Pradesh), India.
Manuscript received on 26 February 2019 | Revised Manuscript received on 13 March 2019 | Manuscript Published on 17 March 2019 | PP: 19-22 | Volume-7 Issue-ICETESM18, March 2019 | Retrieval Number: ICETESM06|19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The new Paradiagram in the internet technology is Internet of Things, where anything that exists in the world can be connected to the internet with unique identity. It has major applications in the fields like health care, agricultural, retail and automation, etc., Here; we proposed a model for Decision Support System (DSS) which used in the Internet of Things (IoT) based agricultural application. It leverages the deep analytics of smart watering equipment data which was collected from the thing speak cloud platform and to improve the water usage and to develop the fields productions in the agricultural fields. The proposed model was evaluated empirically and demonstrated efficiency by using machine learning prediction model approaches. The proposed model was compared with other classifiers and the result shows the efficiency of the system.
Keywords: IoT, Machine Leaning, Deep Analytics, Decision Support System, Agricultural Fields, Thing Speak.
Scope of the Article: Machine Learning