Machine Learning Approach for Agricultural IoT
Abraham Sudharson Ponraj1, Vigneswaran T2
1Abraham Sudharson Ponraj, Assistant Professor at Vellore Institute of Technology, Chennai, (Tamil Nadu) India.
2Dr. Vigneswaran. T Professor, Vellore Institute of Technology, Chennai, (Tamil Nadu) India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 383-392 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2247037619/19©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 rapid growth of Internet of Things (IoT) devices in cities, homes, buildings, industries, health care, automotive and also in agricultural farms have paved the way for deployment of wide range of sensors in them. In return IoT turns out to be the major contributor of new data in any of these fields. A data driven farm management techniques will in turn help in increasing the agricultural yield by planning the input cost, reducing loss and efficient use of resources. IoT on top of increasing the volume of data it also give rise to big data with varied characteristics based on time and locality. To increase the agricultural yield by smart farm management astute analysis and processing of the data generated becomes imperative. With high performance computing at machine learning has created new opportunities for data intensive science. Machine learning will help the farm management system to achieve its goal by exploiting the data that is continuously made available with the help of Agricultural IoT(AIoT) platform and helps the farmer with insights, decisive action and support. This article analyses various existing supervised and unsupervised machine learning techniques applied in agricultural domain and compares one technique with another with respects to accuracy and a confusion matrix is plotted for each.
Keywords: Cluster Analysis; K- Prototype Clustering; Initial Centroid; Number of Cluster; Frequency based Similarity Measurement.
Scope of the Article: IoT