dc.contributor.advisor | Alam, Md. Golam Rabiul | |
dc.contributor.author | Ahmed, Md. Sabbir | |
dc.contributor.author | Tazwar, Md. Tasin | |
dc.contributor.author | Khan, Haseen | |
dc.contributor.author | Roy, Swadhin | |
dc.contributor.author | Iqbal, Junaed | |
dc.date.accessioned | 2021-10-18T08:53:32Z | |
dc.date.available | 2021-10-18T08:53:32Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021-01 | |
dc.identifier.other | ID 17101379 | |
dc.identifier.other | ID 20241038 | |
dc.identifier.other | ID 17101451 | |
dc.identifier.other | ID 17101401 | |
dc.identifier.other | ID 20241048 | |
dc.identifier.uri | http://hdl.handle.net/10361/15367 | |
dc.description | This 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.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 41-42). | |
dc.description.abstract | The 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.statementofresponsibility | Md.Sabbir Ahmed | |
dc.description.statementofresponsibility | Md. Tasin Tazwar | |
dc.description.statementofresponsibility | Haseen Khan | |
dc.description.statementofresponsibility | Swadhin Roy | |
dc.description.statementofresponsibility | Junaed Iqbal | |
dc.format.extent | 42 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 | Population Growth | en_US |
dc.subject | Cultivable Lands | en_US |
dc.subject | Precision Agriculture | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | XGBoost | en_US |
dc.subject | Support Vector Machine | en_US |
dc.subject | Yield Prediction | en_US |
dc.subject.lcsh | Machine learning | |
dc.title | Yield prediction for precision agriculture using extreme gradient boosting and support vector regression | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |