Application of support vector machine for evaulation of agricultural productivity in the state of tamilnadu
Author(s) -
G. Manimannan,
ARUL KUMAR C,
LAKSHMI PRIYA R
Publication year - 2017
Publication title -
journal of management and science
Language(s) - English
Resource type - Journals
eISSN - 2250-1819
pISSN - 2249-1260
DOI - 10.26524/jms.2017.2
Subject(s) - agriculture , livelihood , productivity , population , agricultural economics , gross domestic product , tamil , geography , agricultural productivity , business , socioeconomics , economic growth , economics , demography , linguistics , philosophy , archaeology , sociology
This research paper attempts to identify the agriculture productivity performance in the state of Tamilnadu as agriculture sector is facing so many challenges in the past decades. Most of the agricultural lands are converted into real estate business and also occupied by corporate sector people. Most of the farmers and allied deparment population migrated to other state and even to other countries to live their livelihood, they work as daily wages. In this connection, this research paper attempts to promote agricultural sector as it is the mainstay and backbone of the Indian and Tamilnadu economy. Agriculture plays a vital role in the development of a country as well the state of Tamilnadu. It contributes nearly fifteen percent of Gross Domestic Product (GDP) of India. Seventy percent of the population depends on agriculture for their livelihood. In the past decade agriculture production had faced an increasing trend in districts of Tamil Nadu in all the crops. But nowadays the yield rate has a decreasing trend in Tamilnadu. However, agriculture productivity differs from region to region, which needs a detailed investigation. The main objective of this research paper is to analyze the agriculture productivity of fifteen major Crops in Tamilnadu using Support Vector Machine for district wise classification of entire state of Tamilnadu and Mosaic graph to visualize the performance of agricultural database. The secondary sources of database were collected from Department of Economics and Statistics, Tamilnadu during the period of 2003 to 2012. In this study yield deviation, visualization and classification of fifteen major crops are considered. The results attained three different methods of classifications and are labelled as High, Moderate and Low based on their Enyedi‘s index method of various crops.
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