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Apple Leaf Disease Detection Using Convolutional Neural

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dc.contributor.author Akhter, Moriom
dc.date.accessioned 2022-11-17T05:19:01Z
dc.date.available 2022-11-17T05:19:01Z
dc.date.issued 22-09-01
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8951
dc.description.abstract Being totally dependent on agriculture, Bangladesh, detecting plant diseases can help farmers stimulate economic growth. This study explains how to recognize Apple leaf disease using deep learning algorithms. Deep learning algorithms can properly detect the defective leaf photos, which may enable the farmers, diagnose the leaf disease accurately and take urgent precautions in accordance with the disease. In order to classify each illness in our article, we first gathered the photos from the nursery and preprocessed them so that they fit the specific model that we employed. Later, in order to achieve a better result, we changed and applied a CNN model that was consistent with our dataset. By doing this, we can testify the diseased leaves 99.05% accurately. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Agriculture en_US
dc.subject Economic growth en_US
dc.subject Algorithms en_US
dc.subject Learning en_US
dc.title Apple Leaf Disease Detection Using Convolutional Neural en_US
dc.type Other en_US


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