Crop yield prediction using machine learning and deep learning
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| 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.contributor.department | Department of Computer Science and Engineering | |
| 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.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 34-35). | |
| 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.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.degree | Bachelor of Science in Computer Science and Engineering | |
| 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.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.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 |