Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Detaylı Bibliyografya
Asıl Yazarlar:	Hasan, Raad, Hossain, Romaiya, Ahmed, Farah, Md. Iftekhar Islam, Md. Iftekhar
Diğer Yazarlar:	Rahman, Md. Khalilur
Materyal Türü:	Tez
Dil:	English
Baskı/Yayın Bilgisi:	Brac University 2021
Konular:	Machine Learning Weather Prediction Land Area vs Crop Production Prediction Machine learning
Online Erişim:	http://hdl.handle.net/10361/14992

id	10361-14992
record_format	dspace
spelling	10361-149922022-01-26T10:18:21Z Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops Hasan, Raad Hossain, Romaiya Ahmed, Farah Md. Iftekhar Islam, Md. Iftekhar Rahman, Md. Khalilur Department of Computer Science and Engineering, Brac University Machine Learning Weather Prediction Land Area vs Crop Production Prediction Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 46-48). Bangladesh, being an Agro-Economy based country, is lacking the technological improvements regarding Supply-Demand ratio of Food Supply to the citizens. Since the recent price hike of onions in 2019, it is evident that such technological disadvantages bring severe losses to the projection of demand of food items and can bring significant hindrance to the economy of the country, adversely affecting the daily lives of the people. Therefore, we propose this paper to bridge the gap between Production, Consumption, Import-Export and Supply-Demand ratio using Linear Regression, Polynomial Regression, and Random Forest Techniques on datasets including and not limited to Average Min-Max Monthly Temperature, Average Monthly Rainfall and Humidity, Land Area-Production-Yield ratios, and Daily Consumption etc. This static dataset contains previous years’ data taken from the Yearbook of Agricultural Statistics and Bangladesh Agricultural Research Council of those crops according to the area. The six year production period of 2013 to 2018 and the weather data set of 2008-2018 of Bangladesh have been taken into account in the formation of this dataset to corroborate learning and training of the algorithms and elevating the accuracy rate of the projection. The Cultivation Area estimates acquired are to be cross-verified with Satellite Images. Raad Hasan Romaiya Hossain Farah Ahmed Md. Iftekhar Islam B. Computer Science 2021-09-09T12:02:12Z 2021-09-09T12:02:12Z 2021 2021-06 Thesis ID19301278 ID 17101468 ID 17101280 ID 16201072 http://hdl.handle.net/10361/14992 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine Learning Weather Prediction Land Area vs Crop Production Prediction Machine learning
spellingShingle	Machine Learning Weather Prediction Land Area vs Crop Production Prediction Machine learning Hasan, Raad Hossain, Romaiya Ahmed, Farah Md. Iftekhar Islam, Md. Iftekhar Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Rahman, Md. Khalilur
author_facet	Rahman, Md. Khalilur Hasan, Raad Hossain, Romaiya Ahmed, Farah Md. Iftekhar Islam, Md. Iftekhar
format	Thesis
author	Hasan, Raad Hossain, Romaiya Ahmed, Farah Md. Iftekhar Islam, Md. Iftekhar
author_sort	Hasan, Raad
title	Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
title_short	Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
title_full	Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
title_fullStr	Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
title_full_unstemmed	Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
title_sort	use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14992
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_version_	1814308816350609408

Use of machine learning and weather predictions to bridge the gap between consumer demands and the market requirements of food crops

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