JASIC Volume. 5, Issue 1 (2024)

  1. Home
  2. Volume. 5, Issue 1

Contributor(s)

Damilare Andrew Omideyi, Rafiu Mope Isiaka & Ronke Seyi Babatunde
 

Keywords

Convolutional Neural Network Newcastle disease deep learning model fecal image image data.
 

Download Full-text (PDF)

... Download File [ 0.30 MB ]
 
Go Back

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

Abstract: An 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.
 
 
 

Journal of Applied Science, Information and Computing (ISSN : 3007-8903) (JASIC), 2024