Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice

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

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Bhattacharjee, Ruposri, Mamun, Kazi Andelib, Asif, Kazi Saad, Khan, Shaian
Άλλοι συγγραφείς:	Mohsin, Abu S.M.
Μορφή:	Thesis
Γλώσσα:	English
Έκδοση:	Brac University 2024
Θέματα:	Aus Aman Boro Yield prediction Machine learning, and agriculture KNN Linear regression XGBoost Random forest ARIMA SARIMAX Machine learning
Διαθέσιμο Online:	http://hdl.handle.net/10361/22424

id	10361-22424
record_format	dspace
spelling	10361-224242024-02-12T21:03:20Z Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice Bhattacharjee, Ruposri Mamun, Kazi Andelib Asif, Kazi Saad Khan, Shaian Mohsin, Abu S.M. Department of Electrical and Electronic Engineering, Brac University Aus Aman Boro Yield prediction Machine learning, and agriculture KNN Linear regression XGBoost Random forest ARIMA SARIMAX Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 81-82). Agriculture has been the driving force of the Bangladesh economy. In the agricultural sector, farmers are largely incapable of using scientific technology to maximize crop yield and identify which crops can be grown in specific weather and soil conditions. Recently, the effectiveness of machine learning-based algorithms in utilizing large datasets to accurately predict and provide descriptive solutions holds promising potential in solving this problem by giving descriptive farming advice and fertilizer usage for farmers and proper yield predictions for better import and export policies. Therefore, this paper aims to use historical weather and climate data (such as temperature, rainfall, average bright sunshine, cloud coverage, etc.) and agricultural data such as fertilizer, soil type, and soil moisture to provide predictions on the yield of Aus, Boro, and Aman that can be expected to grow in a region as well as predict the future rice prices of Dhaka depending on existing data. After analysis it was found that there is direct correlation of high accuracy between weather factors such as average rainfall, average minimum temperature, average maximum temperature, average yearly temperature, average bright sunshine, average cloud coverage, relative humidity, average wind speed, latitude, longitude and altitude and yearly yield of Aus, Aman and Boro rice when algorithms such as KNN, linear regression, random forest, and XGBoost were implemented. Furthermore, correlation was found among soil type, soil moisture, fertilizer type and crop yield. Finally, a price prediction of three different types of rice –Aus, Aman, and Boro – between Dhaka and Delhi was conducted using models such as ARIMA and SARIMAX. Ruposri Bhattacharjee Kazi Andelib Mamun Kazi Saad Asif Shaian Khan B. Electrical and Electronic Engineering 2024-02-12T10:19:23Z 2024-02-12T10:19:23Z 2021 2021-09 Thesis ID: 18321060 ID: 17121017 ID: 16321081 ID: 17321031 http://hdl.handle.net/10361/22424 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. 83 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Aus Aman Boro Yield prediction Machine learning, and agriculture KNN Linear regression XGBoost Random forest ARIMA SARIMAX Machine learning
spellingShingle	Aus Aman Boro Yield prediction Machine learning, and agriculture KNN Linear regression XGBoost Random forest ARIMA SARIMAX Machine learning Bhattacharjee, Ruposri Mamun, Kazi Andelib Asif, Kazi Saad Khan, Shaian Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.
author2	Mohsin, Abu S.M.
author_facet	Mohsin, Abu S.M. Bhattacharjee, Ruposri Mamun, Kazi Andelib Asif, Kazi Saad Khan, Shaian
format	Thesis
author	Bhattacharjee, Ruposri Mamun, Kazi Andelib Asif, Kazi Saad Khan, Shaian
author_sort	Bhattacharjee, Ruposri
title	Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
title_short	Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
title_full	Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
title_fullStr	Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
title_full_unstemmed	Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
title_sort	machine learning based analysis and prediction of crop yield and prices of aman, aus and boro rice
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22424
work_keys_str_mv	AT bhattacharjeeruposri machinelearningbasedanalysisandpredictionofcropyieldandpricesofamanausandbororice AT mamunkaziandelib machinelearningbasedanalysisandpredictionofcropyieldandpricesofamanausandbororice AT asifkazisaad machinelearningbasedanalysisandpredictionofcropyieldandpricesofamanausandbororice AT khanshaian machinelearningbasedanalysisandpredictionofcropyieldandpricesofamanausandbororice
_version_	1814308340407205888

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice

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