Machine Learning Image Classification model to Identify Cattle in Kenya

Classifying cattle using muzzle images is an emerging technology in livestock management for recognition and classification. This study used Convolutional Neural Networks (CNN) algorithm to uniquely identify cattle by using their muzzle patterns which are unique to every single cattle. The study used a dataset of 4,923 muzzle images of different cattle breeds which were pre-processed to improve the dataset’s performance and reduce overfitting. The Convolution Neural Network used several convolutional layers to capture muzzle patterns, pooling and dense layers to differentiate breeds. Adam optimizer and categorical cross-entropy loss were employed for model training. The results revealed high accuracy, verifying muzzle images as an effective biometric method for cattle identification. Transfer learning via pre-trained models positively impacted model accuracy and generalization. The technology can be integrated into livestock management and breeding programs, as well as agricultural and farming systems.

Biometric Identification, Muzzle Images, Convolutional Neural Networks, Keras Framework.

1. KIPPRA. (2024, January 8). Banditry and lawlessness in arid and semi-arid lands of Kenya: Which way out? Retrieved from https://kippra.or.ke/banditry-and-lawlessness-in-arid-and-semi-arid-lands-of-kenya-which-way-out/ [Google Scholar] [Crossref]

2. Alsaadi, E. M. T. A., & El Abbadi, N. K. (2020). An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning. Iraqi Journal of Science, 2361–2370. [Google Scholar] [Crossref]

3. Bello, R., & Abubakar, S. (2019). Development of a software package for cattle identification in Nigeria. Journal of Applied Sciences and Environmental Management, 23(10), 1825–1828. [Google Scholar] [Crossref]

4. Crall, J. P., Stewart, C. V., Berger-Wolf, T. Y., Rubenstein, D. I., & Sundaresan, S. R. (2013). Hotspotter—Patterned species instance recognition, 230–237. [Google Scholar] [Crossref]

5. Huang, Y.-P., & Basanta, H. (2019). Bird Image Retrieval and Recognition Using a Deep Learning Platform. IEEE Access, 7, 66980–66989. https://doi.org/10.1109/ACCESS.2019.2918274 [Google Scholar] [Crossref]

6. Parham, J., & Stewart, C. (2016). Detecting plains and Grevy’s Zebras in the real world. 1–9. [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition