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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
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    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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    19301278, 17101468, 17101280, 16201072_CSE.pdf (1.598Mb)
    Date
    2021-06
    Publisher
    Brac University
    Author
    Hasan, Raad
    Hossain, Romaiya
    Ahmed, Farah
    Md. Iftekhar Islam, Md. Iftekhar
    Metadata
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    URI
    http://hdl.handle.net/10361/14992
    Abstract
    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.
    Keywords
    Machine Learning; Weather Prediction; Land Area vs; Crop Production Prediction
     
    LC Subject Headings
    Machine learning
     
    Description
    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).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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