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Plant Disease Classification using Deep Learning

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dc.contributor.author Ahmed, Radia
dc.contributor.author Islam, Tariqul
dc.contributor.author Siam, Mir
dc.date.accessioned 2022-10-03T05:18:15Z
dc.date.available 2022-10-03T05:18:15Z
dc.date.issued 2020-06-26
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3738
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract In a country like Bangladesh plant disease is a common factor. The timely and accurate diagnosis of plant disease plays a very important role in preventing the loss of productivity and loss of reduce quality of agricultural product. Till now many Machine learning (ML) models have been employed for the detection and classification of plant disease. For advancement Deep learning (DL) has also employed in this research area and it has shown a vital impact in disease detection accuracy. In this paper we classified ‘15’ species of crops from 20,069 images. The dataset have been taken from plant village. We have considered here Support Vector machine which is a supervised learning algorithm for classification and regression.We also used Sequential Model for detection. We have used Train-Test-split model to train the dataset and we achieved 92.5% accuracy. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ECE00242
dc.subject Plant Disease, agricultural product, Machine learning, Deep learning en_US
dc.title Plant Disease Classification using Deep Learning en_US
dc.type Thesis en_US


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