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Crop yield prediction using machine learning and deep learning

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorZaman, Shakila
dc.contributor.advisorShakil, Arif
dc.contributor.authorSaha, Sarna
dc.contributor.authorIslam, Md. Asiful
dc.contributor.authorAnjum, Nishat
dc.contributor.authorMitul, Mahmudul Hasan
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-02-23T05:46:47Z
dc.date.available2025-02-23T05:46:47Z
dc.date.copyright2023
dc.date.issued2023-01
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 34-35).
dc.descriptionThis 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.abstractBangladesh 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.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilitySarna Saha
dc.description.statementofresponsibilityMd. Asiful Islam
dc.description.statementofresponsibilityNishat Anjum
dc.description.statementofresponsibilityMahmudul Hasan Mitul
dc.format.extent35 pages
dc.identifier.otherID 22141051
dc.identifier.otherID 17201077
dc.identifier.otherID 18101431
dc.identifier.otherID 18101066
dc.identifier.urihttp://hdl.handle.net/10361/25534
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC 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.subjectCrop yield predictionen_US
dc.subjectNeural networksen_US
dc.subjectAIen_US
dc.subjectMLen_US
dc.subjectClassification modelsen_US
dc.subjectRecurrent neural networksen_US
dc.subject.lcshArtificial intelligence
dc.subject.lcshCognitive learning theory
dc.subject.lcshMachine learning
dc.titleCrop yield prediction using machine learning and deep learningen_US
dc.typeThesisen_US

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