Deep Learning Based Classification of Poultry Disease

Khushi Srivastava; Parth Pandey

doi:10.5875/ausmt.v13i1.2439

Deep Learning Based Classification of Poultry Disease

Khushi Srivastava A¹* and Parth Pandey B¹

¹Department of computer science and engineering, Faculty of engineering and technology, University of lucknow, Lucknow, India

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ABSTRACT

Poultry diseases including Coccidiosis, Salmonella, and Newcastle can lower chicken productivity if they are not detected early on. Deep learning algorithms can assist with the early identification of diseases. In this study, a Convolutional Neural Network based framework has been proposed to classify poultry diseases by distinguishing healthy and unhealthy fecal images. Unhealthy images can be a sign of the poultry diseases. The Image Classification dataset was used to train the framework, and it was discovered that it performed with an accuracy of 99.99%, 96.05%, 93.23% on the training set, validation set, testing set respectively. When the proposed network's performance was evaluated against pre-trained models, it was discovered that the proposed model was unquestionably the best one for classifying chicken disease. This framework can beat resource-intensive machine learning methods due to the trained model's reduced weight and can be implemented with a small amount of memory and computational power.

Keywords: Poultry diseases, Deep learning, Machine learning, Artificial Intelligence, Fecal images, Convolutional neural network.

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ARTICLE INFORMATION

Accepted: 2023-05-01
Available Online: 2023-05-01

Cite this article:

Srivastava, K., and Pandey, P. (2023) Deep Learning Based Classification of Poultry Disease. Int. j. autom. smart technol. https://doi.org/10.5875/ausmt.v13i1.2439

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.