Show simple item record

dc.contributor.advisorAlam, Md. Golam Rabiul
dc.contributor.authorAhmed, Md. Sabbir
dc.contributor.authorTazwar, Md. Tasin
dc.contributor.authorKhan, Haseen
dc.contributor.authorRoy, Swadhin
dc.contributor.authorIqbal, Junaed
dc.date.accessioned2021-10-18T08:53:32Z
dc.date.available2021-10-18T08:53:32Z
dc.date.copyright2021
dc.date.issued2021-01
dc.identifier.otherID 17101379
dc.identifier.otherID 20241038
dc.identifier.otherID 17101451
dc.identifier.otherID 17101401
dc.identifier.otherID 20241048
dc.identifier.urihttp://hdl.handle.net/10361/15367
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 41-42).
dc.description.abstractThe rate of population growth of southern Asia is rising dramatically. As a part of this area, Bangladesh is no different. Moreover, the cultivable lands are declining at a huge rate. So to maintain the balance between the food production and consumer demand, we need to know the yield of the crop earlier to maintain the balance as well as ensuring the food security of the people. Hence, food production in a precise manner needs to be introduced to get more production in a small amount of land. From this concept “Precision Agriculture” term has come. Since rice is the staple food of Bangladesh so this research tries to demonstrate precision agriculture in terms of paddy. This research proposes a system which is capable of predicting yield of paddy based on different parameters. For this prediction, two machine learning approaches are used, such as XGBoost and Support Vector Machine (SVM) that can predict the yield of aus, aman and boro based on the relevant features. The main objective of this system is to optimum paddy production using the minimum inputs to demonstrate precision agriculture in terms of paddy production. The result of the prediction will assist the farmers to take necessary steps if needed to increase the production. Again, the prediction result will help the government to take their decisions regarding agricultural perspective. There is some research in precision agriculture, however, there exist many scopes to use machine learning techniques to predict the yield of the harvest which will eventually help them economically. Therefore, this research focuses on developing an intelligent system for precision agriculture of paddy using yield prediction of it.en_US
dc.description.statementofresponsibilityMd.Sabbir Ahmed
dc.description.statementofresponsibilityMd. Tasin Tazwar
dc.description.statementofresponsibilityHaseen Khan
dc.description.statementofresponsibilitySwadhin Roy
dc.description.statementofresponsibilityJunaed Iqbal
dc.format.extent42 pages
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.subjectPopulation Growthen_US
dc.subjectCultivable Landsen_US
dc.subjectPrecision Agricultureen_US
dc.subjectMachine Learningen_US
dc.subjectXGBoosten_US
dc.subjectSupport Vector Machineen_US
dc.subjectYield Predictionen_US
dc.subject.lcshMachine learning
dc.titleYield prediction for precision agriculture using extreme gradient boosting and support vector regressionen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record