Publication:
Comparison of Convolutional Neural Network Performance Against Deep Learning Architectecture With Diseased Paddy Dataset

dc.contributor.authorAin Nadia Alin binti Mohd Azemanen_US
dc.date.accessioned2023-05-03T16:31:45Z
dc.date.available2023-05-03T16:31:45Z
dc.date.issued2020-09
dc.descriptionInterim Semester 2020/2021en_US
dc.description.abstractPaddy farmers experience a big loss in terms of time, energy, and cost when they experienced food spoilage. During harvesting, they discovered that the paddy they planted turns out to be defective due to diseases. Their wasted time energy and money could have been prevented if they could foresee these paddy diseases. Hence, Artificial Intelligence is introduced to help them reduce their losses. There are three types of paddy diseases detected in this project, they are the Bacterial Leaf Bight, Brown Spot Disease, and the Leaf Blast Disease. The aim was to train an AI model that could produce a minimum of 20% mean average precision (mAP) value and to create a parameter that can be applied to the datasets to obtain consistent results. There were four models trained and observed. The model that obtained the highest mAP value of 0.2222 was the faster RCNN Mobilenet v2, however, it also took the longest to complete the training. There were three datasets used to test the parameter that could be applied to obtain consistent results. It was observed that dataset B (contains only augmented images) achieved the highest mAP value of 0.2061 or roughly 21% when trained using the SSD Mobilenet v1 model.en_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21308
dc.language.isoenen_US
dc.subjectNeural Networken_US
dc.titleComparison of Convolutional Neural Network Performance Against Deep Learning Architectecture With Diseased Paddy Dataseten_US
dspace.entity.typePublication
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