An enhanced image based mobile deep learning model for identification of Newcastle poultry disease

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dc.contributor.authorDamilare Andrew Omideyi, Rafiu Mope Isiaka, Ronke Seyi Babatunde
dc.date.accessioned2024-07-20T13:28:07Z
dc.date.available2024-07-20T13:28:07Z
dc.date.issued2024-06-11
dc.description.abstractAn enhanced mobile deep learning model based on images is presented in this paper to identify Newcastle poultry disease. A dataset of manually annotated and labeled images of the disease was utilized to pre-train an image-based Convolutional Neural Network (CNN). An Android smartphone app was developed to communicate with the model. A local server was integrated with the generated model to do image classification. A mobile application was developed and made available, enabling users to upload a fecal photograph to a website housed on the streamlet server and obtain the model's processed findings. The user regains control over their health status. The model achieved an accuracy of 95% on the test set and was able to correctly identify specific instances of Newcastle poultry disease. The paper discusses the advantages of a mobile-based approach in comparison to traditional methods of identification and proposes the model as an effective low-cost solution for farmers and researchers.
dc.identifier.citationOmideyi et. al., 2024
dc.identifier.urihttps://doi.org/10.59568/JASIC-2024-5-1-03
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/1693
dc.language.isoen
dc.publisherPublished by School of Mathematics and Computing, Kampala International University
dc.titleAn enhanced image based mobile deep learning model for identification of Newcastle poultry disease
dc.typeArticle
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