dc.contributor.advisor | Zaman, Shakila | |
dc.contributor.advisor | Shakil, Arif | |
dc.contributor.author | Saha, Sarna | |
dc.contributor.author | Islam, Md. Asiful | |
dc.contributor.author | Anjum, Nishat | |
dc.contributor.author | Mitul, Mahmudul Hasan | |
dc.date.accessioned | 2025-02-23T05:46:47Z | |
dc.date.available | 2025-02-23T05:46:47Z | |
dc.date.copyright | 2023 | |
dc.date.issued | 2023-01 | |
dc.identifier.other | ID 22141051 | |
dc.identifier.other | ID 17201077 | |
dc.identifier.other | ID 18101431 | |
dc.identifier.other | ID 18101066 | |
dc.identifier.uri | http://hdl.handle.net/10361/25534 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 34-35). | |
dc.description.abstract | Bangladesh is an agrarian country. Though, a substantial portion of our economy
and workforce depends directly or indirectly on agriculture. However, due to climate
change, floods, insufficient incentives, and less grist our farmers are getting demotivated
in farming. As a result, more and more farmers are leaving the agriculture
sector every year and this can cause devastating effects for Bangladesh. Moreover,
there is little or no research on improving Bangladesh agriculture using cutting-edge
machine learning techniques. So, this research works on Crop yield prediction Using
Machine learning and deep learning. This work explores the different state-of-the-art
machine learning and deep learning techniques and relevant dataset to develop an
effective Crop yield prediction system for Bangladeshi farmers. So that our farmers
can decide which crop to cultivate for gaining the maximum yield by following our
prediction system. | en_US |
dc.description.statementofresponsibility | Sarna Saha | |
dc.description.statementofresponsibility | Md. Asiful Islam | |
dc.description.statementofresponsibility | Nishat Anjum | |
dc.description.statementofresponsibility | Mahmudul Hasan Mitul | |
dc.format.extent | 35 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | 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. | |
dc.subject | Crop yield prediction | en_US |
dc.subject | Neural networks | en_US |
dc.subject | AI | en_US |
dc.subject | ML | en_US |
dc.subject | Classification models | en_US |
dc.subject | Recurrent neural networks | en_US |
dc.subject.lcsh | Artificial intelligence | |
dc.subject.lcsh | Cognitive learning theory | |
dc.subject.lcsh | Machine learning | |
dc.title | Crop yield prediction using machine learning and deep learning | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B.Sc. in Computer Science and Engineering | |