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Machine Learning-Based Decision Support System for Effective Quality Farming

Machine Learning-Based Decision Support System for Effective Quality Farming

Balaji Prabhu B. V., M. Dakshayini
Copyright: © 2021 |Volume: 13 |Issue: 1 |Pages: 28
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781799860167|DOI: 10.4018/IJGHPC.2021010105
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MLA

Balaji Prabhu B. V., and M. Dakshayini. "Machine Learning-Based Decision Support System for Effective Quality Farming." IJGHPC vol.13, no.1 2021: pp.82-109. http://doi.org/10.4018/IJGHPC.2021010105

APA

Balaji Prabhu B. V. & Dakshayini, M. (2021). Machine Learning-Based Decision Support System for Effective Quality Farming. International Journal of Grid and High Performance Computing (IJGHPC), 13(1), 82-109. http://doi.org/10.4018/IJGHPC.2021010105

Chicago

Balaji Prabhu B. V., and M. Dakshayini. "Machine Learning-Based Decision Support System for Effective Quality Farming," International Journal of Grid and High Performance Computing (IJGHPC) 13, no.1: 82-109. http://doi.org/10.4018/IJGHPC.2021010105

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Abstract

Although Big data analytics, machine learning and cloud technologies have been acknowledged as better enablers in revolutionizing the quality of agricultural systems, in most of the developing nations like India there is no able system to effectively survey the real grocery needs of the society and accordingly educate the farmers to grow and supply the crops. Due to lack of such process, there is no synchronization between demand and supply of food crops, and hence, most of the time farmers suffer with loss and consumers suffer from high varied prices. In order to address this problem, data about the demand, supply, and price variation of various crops of different seasons of the year have been collected and analysed. The analysis results have shown a huge gap between demand and supply of crops. Hence, this work proposes novel machine learning-based data analytics system that forecasts the demand for different food crops and regulates the supply accordingly by assisting the farmers in growing the crops based on the demand. Implementation results have shown 92% reduction in the gap.

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