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Article

Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs

1
Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
2
School of Computing, Southern Illinois University, Carbondale, IL 62901, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(14), 8192; https://doi.org/10.3390/app13148192
Submission received: 5 June 2023 / Revised: 11 July 2023 / Accepted: 12 July 2023 / Published: 14 July 2023
(This article belongs to the Special Issue Applications of Machine Learning in Agriculture)

Abstract

Image-based Arabian camel breed classification is an important task for various practical applications, such as breeding management, genetic improvement, conservation, and traceability. However, it is a challenging task due to the lack of standardized criteria and methods, the high similarity among breeds, and the limited availability of data and resources. In this paper, we propose an approach to tackle this challenge by using convolutional neural networks (CNNs) and transfer learning to classify images of six different Arabian camel breeds: Waddeh, Majaheem, Homor, Sofor, Shaele, and Shageh. To achieve this, we created, preprocessed, and annotated a novel dataset of 1073 camel images. We then pre-trained CNNs as feature extractors and fine-tuned them on our new dataset. We evaluated several popular CNN architectures with diverse characteristics such as InceptionV3, NASNetLarge, PNASNet-5-Large, MobileNetV3-Large, and EfficientNetV2 (small, medium, and large variants), and we found that NASNetLarge achieves the best test accuracy of 85.80% on our proposed dataset. Finally, we integrated the best-performing CNN architecture, NASNetLarge, into a mobile application for further validation and actual use in a real-world scenarios.
Keywords: transfer learning; convolutional neural networks; image classification; camel; InceptionV3; NASNetLarge; PNASNet; MobileNetV3; EfficientNetV2 transfer learning; convolutional neural networks; image classification; camel; InceptionV3; NASNetLarge; PNASNet; MobileNetV3; EfficientNetV2

Share and Cite

MDPI and ACS Style

Alfarhood, S.; Alrayeh, A.; Safran, M.; Alfarhood, M.; Che, D. Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs. Appl. Sci. 2023, 13, 8192. https://doi.org/10.3390/app13148192

AMA Style

Alfarhood S, Alrayeh A, Safran M, Alfarhood M, Che D. Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs. Applied Sciences. 2023; 13(14):8192. https://doi.org/10.3390/app13148192

Chicago/Turabian Style

Alfarhood, Sultan, Atheer Alrayeh, Mejdl Safran, Meshal Alfarhood, and Dunren Che. 2023. "Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs" Applied Sciences 13, no. 14: 8192. https://doi.org/10.3390/app13148192

APA Style

Alfarhood, S., Alrayeh, A., Safran, M., Alfarhood, M., & Che, D. (2023). Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs. Applied Sciences, 13(14), 8192. https://doi.org/10.3390/app13148192

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