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Exploring Machine Learning Approaches on Crop Pattern Recognition

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dc.contributor.author Kabir, Kazi Hasibul
dc.contributor.author Sultana, Sharmin
dc.contributor.author Aqib, Md. Zahiruddin
dc.date.accessioned 2019-03-04T07:43:38Z
dc.date.available 2019-03-04T07:43:38Z
dc.date.issued 4/24/2018
dc.identifier.uri http://dspace.ewubd.edu/handle/2525/2993
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract Agriculture activities monitoring is important to ensure food security. Remote sensing plays a signi cant role for large scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the cropping pattern. Classi cation algorithms are used to classify crop patterns and mapped agriculture land used. Some conventional classi cation methods including support vector machine (SVM) and decision trees were applied for crop pattern recognition. However, in this report, we are proposing di erent machine learning approaches such as Naive Bayes (NB), Deep Neural Network (DNN) and Random Forest (RF) classi cation to improve and nd a better solution for crop pattern recognition. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00169
dc.subject Machine Learning Approaches on Crop Pattern Recognition en_US
dc.title Exploring Machine Learning Approaches on Crop Pattern Recognition en_US
dc.type Thesis en_US


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