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.
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.