Yield prediction for precision agriculture using extreme gradient boosting and support vector regression

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliografiset tiedot
Päätekijät:	Ahmed, Md. Sabbir, Tazwar, Md. Tasin, Khan, Haseen, Roy, Swadhin, Iqbal, Junaed
Muut tekijät:	Alam, Md. Golam Rabiul
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	Brac University 2021
Aiheet:	Population Growth Cultivable Lands Precision Agriculture Machine Learning XGBoost Support Vector Machine Yield Prediction Machine learning
Linkit:	http://hdl.handle.net/10361/15367

id	10361-15367
record_format	dspace
spelling	10361-153672022-01-26T10:16:00Z Yield prediction for precision agriculture using extreme gradient boosting and support vector regression Ahmed, Md. Sabbir Tazwar, Md. Tasin Khan, Haseen Roy, Swadhin Iqbal, Junaed Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Population Growth Cultivable Lands Precision Agriculture Machine Learning XGBoost Support Vector Machine Yield Prediction Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-42). 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. Md.Sabbir Ahmed Md. Tasin Tazwar Haseen Khan Swadhin Roy Junaed Iqbal B. Computer Science 2021-10-18T08:53:32Z 2021-10-18T08:53:32Z 2021 2021-01 Thesis ID 17101379 ID 20241038 ID 17101451 ID 17101401 ID 20241048 http://hdl.handle.net/10361/15367 en 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. 42 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Population Growth Cultivable Lands Precision Agriculture Machine Learning XGBoost Support Vector Machine Yield Prediction Machine learning
spellingShingle	Population Growth Cultivable Lands Precision Agriculture Machine Learning XGBoost Support Vector Machine Yield Prediction Machine learning Ahmed, Md. Sabbir Tazwar, Md. Tasin Khan, Haseen Roy, Swadhin Iqbal, Junaed Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Ahmed, Md. Sabbir Tazwar, Md. Tasin Khan, Haseen Roy, Swadhin Iqbal, Junaed
format	Thesis
author	Ahmed, Md. Sabbir Tazwar, Md. Tasin Khan, Haseen Roy, Swadhin Iqbal, Junaed
author_sort	Ahmed, Md. Sabbir
title	Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
title_short	Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
title_full	Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
title_fullStr	Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
title_full_unstemmed	Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
title_sort	yield prediction for precision agriculture using extreme gradient boosting and support vector regression
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15367
work_keys_str_mv	AT ahmedmdsabbir yieldpredictionforprecisionagricultureusingextremegradientboostingandsupportvectorregression AT tazwarmdtasin yieldpredictionforprecisionagricultureusingextremegradientboostingandsupportvectorregression AT khanhaseen yieldpredictionforprecisionagricultureusingextremegradientboostingandsupportvectorregression AT royswadhin yieldpredictionforprecisionagricultureusingextremegradientboostingandsupportvectorregression AT iqbaljunaed yieldpredictionforprecisionagricultureusingextremegradientboostingandsupportvectorregression
_version_	1814308551256965120

Yield prediction for precision agriculture using extreme gradient boosting and support vector regression

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