Azad, A.K.M Abdul MalekShahrin, FarihaZahin, LabibaRahman, RamisaHossain, A S M Jahir2021-10-192021-10-1920202020-09ID 17121031ID 17121047ID 17121006ID 13121007http://hdl.handle.net/10361/15415Cataloged from PDF version of thesis.Includes bibliographical references (pages 65-69).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2020.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.81 pagesenBrac 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.AgricultureLandsat 8HabiganjCrop monitoringCrop yield predictionK-MeansMask R-CNNTime series modelMachine learningThesis