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A Deep Computer Vision Approach to Detect Eggplant Diseases

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dc.contributor.author Hasnat, Abul
dc.contributor.author Hasan, Mehedi
dc.date.accessioned 2022-02-15T04:19:36Z
dc.date.available 2022-02-15T04:19:36Z
dc.date.issued 2021-09
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7150
dc.description.abstract Farming inputs are very vital, yet they are not always available to farmers. The goal of this research was to construct an intelligence system utilizing the recognition of eggplant diseases utilizing picture treatment techniques in order to educate farmers about eggplant sickness. The lack of data for both disorders encouraged us to develop a standard dataset for two prominent diseases in the laboratory. Pre-trained Eggplant-disease classification Visual Geometry Group 16 (VGG16) resnet50 and inceptionV3 architectures are used in our work. Further, VGG16 was utilized as the 8th convolution layer feature extractor and these features were used to graduate illnesses. An equivalent or in some cases a greater accuracy was shown in the analysis. There have been proposed possible causes for variations in interclass precision and future direction. Our highest accuracy is achieved by VGG16 the accuracy rate is 99.55%. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Farming sector en_US
dc.subject Intelligence system en_US
dc.subject Eggplant diseases en_US
dc.subject Treatment techniques en_US
dc.title A Deep Computer Vision Approach to Detect Eggplant Diseases en_US
dc.type Article en_US

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