Apple Leaf Disease Detection Using Convolutional Neural

dc.contributor.authorAkhter, Moriom
dc.date.accessioned2022-11-17T05:19:01Z
dc.date.available2022-11-17T05:19:01Z
dc.date.issued22-09-01
dc.description.abstractBeing 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.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8951
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8951
dc.language.isoen_US
dc.publisherDaffodil International University
dc.sourceDIU Institutional Repository
dc.subjectAgriculture
dc.subjectEconomic growth
dc.subjectAlgorithms
dc.subjectLearning
dc.titleApple Leaf Disease Detection Using Convolutional Neural
dc.typeOther

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