Application of Machine Learning Techniques for the Prediction of Tomato Price
Namitha Shambhu Bhat1, Sharada G. Kulkarni2, Shubhada S. Kulkarni3
1Namitha Shambhu Bhat, Department of Computer Science and Engineering, KLS’s Gogte Institute of Technology, Belagavi, India.
2Sharada G. Kulkarni, Department of Computer Science and Engineering, KLS’s Gogte Institute of Technology, Belagavi, India.
3Shubhada S. Kulkarni, Department of Computer Science and Engineering, KLS’s Gogte Institute of Technology, Belagavi, India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 14 May 2019 | Manuscript published on 30 May 2019 | PP: 1793-1798 | Volume-8 Issue-1, May 2019 | Retrieval Number: A2157058119/19©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: Agriculture is the main occupation in India. Farmers face many problems including identifying the tomato quality, fixing their price etc. For this purpose, the farmers have to manually monitor the harvest and take advice from experts for detecting the quality of tomato and fixing the price. This manual process does not give agreeable results always and requires continuous monitoring by the experts. Recently Machine learning has been used with image processing techniques for data analysis that has a huge potential. These techniques can be widely adopted in the field of agriculture to detect the quality of the tomato using various algorithms. The goal of this work is to construct an application, which makes the farmers easily know the quality of tomato fruit and present cost in the city. The proposed system uses algorithms such as Linear Regression and Convolution Neural Network with their effective application for tomato price prediction which uses AWS cloud for storage. At the end, the performance of both the algorithms is also compared.
Index Terms: Machine Learning, Classification, Price, Color, Linear Regression, Convolution Neural Network, AWS Cloud Storage.
Scope of the Article: Machine Learning