Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1138
Title: IoT Based Classification and Price Prediction of Organically and Inorganically Grown Vegetables and Fruits
Authors: Wijekoon, W. M. G. S
Wijewardana, L. W. M
Wattegedara, S. L
Kumara, W. A. L. S
Sasini, W
Abeygunawardhana, P. K. W
Keywords: IoT Based Classification
Price Prediction
Organically
Inorganically
Grown Vegetables
Fruits
Issue Date: 16-Dec-2021
Publisher: IEEE
Citation: W. M. G. S. Wijekoon, L. W. M. Wijewardana, S. L. Wattegedara, W. A. L. S. Kumara, W. Sasini and P. K. W. Abeygunawardhana, "IoT Based Classification and Price Prediction of Organically and Inorganically Grown Vegetables and Fruits," 2021 2nd International Informatics and Software Engineering Conference (IISEC), 2021, pp. 1-5, doi: 10.1109/IISEC54230.2021.9672350.
Series/Report no.: 2021 2nd International Informatics and Software Engineering Conference (IISEC);Pages 1-5
Abstract: Food plays a vital role in human life and, foods also play an essential role in promoting health and disease prevention. Especially fruits and vegetables are considered as one of the primary sources of vitamins and minerals. Therefore, humans are given more priority to consume vegetables and fruits. However, in modern days vegetables and fruits are grown both inorganically and organically. Inorganically grown vegetables are less nutritious and also harmful for the health. Therefore, it is not easy to find quality vegetables and fruits in the market at a reasonable price. The proposed solution is to develop a system to classify the vegetables and fruits, check the freshness and the quality, and predict appropriate prices based on food quality. Here mentioned how the Linear Regression, Ridge Regression, and MLP perception models act when predicting prices. Prices are predicted based on food quality grades.
URI: http://rda.sliit.lk/handle/123456789/1138
ISBN: 978-1-6654-0759-5
Appears in Collections:Department of Computer systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications



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