Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 38, Pages: 1-14
Subhadra Mishra1 , Debahuti Mishra1 * and Gour Hari Santra2
1 Siksha 'O' Anusandhan University, Bhubaneswar - 751030, Odisha, India; [email protected]
2 Orissa University of Agriculture and Technology, Bhubaneswar - 751003, Odisha, India; [email protected]
*Author for correspondence
Siksha 'O' Anusandhan University
Objective: This paper has been prepared as an effort to reassess the research studies on the relevance of machine learning techniques in the domain of agricultural crop production. Methods/Statistical Analysis: This method is a new approach for production of agricultural crop management. Accurate and timely forecasts of crop production are necessary for important policy decisions like import-export, pricing marketing distribution etc. which are issued by the directorate of economics and statistics. However one has understand that these prior estimates are not the objective estimates as these estimate requires lots of descriptive assessment based on many different qualitative factors. Hence there is a requirement to develop statistically sound objective prediction of crop production. That development in computing and information storage has provided large amount of data. Findings: The problem has been to intricate knowledge from this raw data , this has lead to the development of new approach and techniques such as machine learning that can be used to unite the knowledge of the data with crop yield evaluation. This research has been intended to evaluate these innovative techniques such that significant relationship can be found by their applications to the various variables present in the data base. Application / Improvement: The few techniques like artificial neural networks, Information Fuzzy Network, Decision Tree, Regression Analysis, Bayesian belief network. Time series analysis, Markov chain model, k-means clustering, k nearest neighbor, and support vector machine are applied in the domain of agriculture were presented.
Keywords: Artificial Neural Network, Decision Tree, Machine Learning, Regression Analysis, Time Series Analysis
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