Prediction of Clusters using SVM Algorithm in Delineation of Management Zones
Krishna Priya C B1, S. Venkateswari2
1Krishna Priya C B, Research Scholar, Noorul Islam Centre for Higher Education, Kumaracoil-629180, Kanyakumari District, Tamil Nadu, India.
2Dr. S. Venkateswari, Department of Computer Science, L N Govt. College, Ponneri, Thiruvallur, Tamil Nadu, India.
Manuscript received on 06 April 2019 | Revised Manuscript received on 13 May 2019 | Manuscript published on 30 May 2019 | PP: 1025-1028 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1136058119/19©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: Management zones in precision agriculture can be defined as the homogenous regions in the particular productivity area. To increase the yield productivity of the agriculture, it is important to manage these agriculture zones effectively. An important technique in analyzing these management zones are clustering. Using clustering it is easy to categorize the productivity area of the crop field. Clustering is a relevant topic in Data mining. Different clustering algorithms can be used to find out the clusters for the management zones. These clusters can be predicted using the algorithms such as Support vector Machine Algorithm.
Index Terms: Machine Learning, Support Vector Machine. Agriculture, Data Mining.
Scope of the Article: Data Mining.