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dc.contributor.advisorChakrabarty, Dr. Amitabha
dc.contributor.authorShakoor, Md. Tahmid
dc.contributor.authorRahman, Karishma
dc.contributor.authorRayta, Sumaiya Nasrin
dc.date.accessioned2017-05-29T05:49:52Z
dc.date.available2017-05-29T05:49:52Z
dc.date.copyright2017
dc.date.issued2017-04-18
dc.identifier.otherID 13101046
dc.identifier.otherID 13101284
dc.identifier.otherID 13141004
dc.identifier.urihttp://hdl.handle.net/10361/8198
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 53-54).
dc.description.abstractFarmers usually select crops for cultivation based on their previous experiences, the better the profit margin of a crop achieved in the past, probability of choosing that crop increases. However, the lack of information about scientific factors that can affect the output and precise knowledge about cultivation, they end up cultivating crops that do not meet the desired profit margin. To help the farmers take decisions that can make their farming more efficient and profitable, this research tries to establish an intelligent information prediction analysis on farming in Bangladesh. Also, it provides an interface to this analysis for the farmers through an android app which also provides necessary information on cultivation procedure, irrigation and fertilization process. The research suggests area based beneficial crop rank before the cultivation process. It indicates the crops that are cost effective for cultivation for a particular area of land. To achieve these results, we are considering six major crops which are Aus, Aman, Boro rice, Potato, Jute and Wheat. The prediction is based on analyzing a static set of data using Supervised Machine Learning techniques. This static data set contains previous years’ data taken from the Yearbook of Agricultural Statistics and Bangladesh Agricultural Research Council of those crops according to the area. The research intents to do a comparative analysis on Decision Tree Learning, K-Nearest Neighbors and Multiple Linear Regression algorithms to obtain these predictions. The past ten years (20042013) of Bangladesh have been considered making this data set to ensure learning and training of the algorithms and increasing the accuracy rate of the prediction and for testing we used three years (2014-2015) for computing accuracy.en_US
dc.description.statementofresponsibilityMd. Tahmid Shakoor
dc.description.statementofresponsibilityKarishma Rahman
dc.description.statementofresponsibilitySumaiya Nssrin Rayta
dc.format.extent54 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectAgricultural informationen_US
dc.subjectData mining techniquesen_US
dc.titleIntelligent agricultural information monitoring using data mining techniquesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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