Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.

Xehetasun bibliografikoak
Egile Nagusiak:	Shahrin, Fariha, Zahin, Labiba, Rahman, Ramisa, Hossain, A S M Jahir
Beste egile batzuk:	Azad, A.K.M Abdul Malek
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2021
Gaiak:	Agriculture Landsat 8 Habiganj Crop monitoring Crop yield prediction K-Means Mask R-CNN Time series model Machine learning
Sarrera elektronikoa:	http://hdl.handle.net/10361/15415

id	10361-15415
record_format	dspace
spelling	10361-154152021-10-19T21:01:27Z Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning Shahrin, Fariha Zahin, Labiba Rahman, Ramisa Hossain, A S M Jahir Azad, A.K.M Abdul Malek Department of Electrical and Electronic Engineering, Brac University Agriculture Landsat 8 Habiganj Crop monitoring Crop yield prediction K-Means Mask R-CNN Time series model Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-69). Bangladesh is predominately an agriculture-based country, which faces uncertain crop yields and inefficient farming infrastructure resulting in adverse effect in food security. Habiganj is selected as the study area because of its vulnerability to floods and drought due to its unique terrain. This paper aims to present a combinational agricultural mapping and monitoring of Habiganj with crop growth and yield prediction. Multi-spectral band images of Habiganj from Landsat 8 are processed and remote sensing indices are extracted. With options of K-means and Mask R-CNN methods, crop growth is evaluated using both Python and MATLAB. Then using two type of machine learning algorithms crop yield of Habiganj is predicted from its existing parameters and the datasets are predicted by using two type of time series model. Furthermore, comparative studies are concluded between two platforms and time series model to determine the most suited environment for this research purpose. Fariha Shahrin Labiba Zahin Ramisa Rahman A S M Jahir Hossain B. Electrical and Electronic Engineering 2021-10-19T05:15:44Z 2021-10-19T05:15:44Z 2020 2020-09 Thesis ID 17121031 ID 17121047 ID 17121006 ID 13121007 http://hdl.handle.net/10361/15415 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. 81 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Agriculture Landsat 8 Habiganj Crop monitoring Crop yield prediction K-Means Mask R-CNN Time series model Machine learning
spellingShingle	Agriculture Landsat 8 Habiganj Crop monitoring Crop yield prediction K-Means Mask R-CNN Time series model Machine learning Shahrin, Fariha Zahin, Labiba Rahman, Ramisa Hossain, A S M Jahir Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.
author2	Azad, A.K.M Abdul Malek
author_facet	Azad, A.K.M Abdul Malek Shahrin, Fariha Zahin, Labiba Rahman, Ramisa Hossain, A S M Jahir
format	Thesis
author	Shahrin, Fariha Zahin, Labiba Rahman, Ramisa Hossain, A S M Jahir
author_sort	Shahrin, Fariha
title	Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
title_short	Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
title_full	Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
title_fullStr	Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
title_full_unstemmed	Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
title_sort	agricultural analysis and crop yield prediction of habiganj using multispectral bands of satellite imagery with machine learning
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15415
work_keys_str_mv	AT shahrinfariha agriculturalanalysisandcropyieldpredictionofhabiganjusingmultispectralbandsofsatelliteimagerywithmachinelearning AT zahinlabiba agriculturalanalysisandcropyieldpredictionofhabiganjusingmultispectralbandsofsatelliteimagerywithmachinelearning AT rahmanramisa agriculturalanalysisandcropyieldpredictionofhabiganjusingmultispectralbandsofsatelliteimagerywithmachinelearning AT hossainasmjahir agriculturalanalysisandcropyieldpredictionofhabiganjusingmultispectralbandsofsatelliteimagerywithmachinelearning
_version_	1814308324938612736

Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning

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