Individual buffalo identification through muzzle dermatoglyphics images using deep learning approaches

Singh, Rana Ranjeet and Fatimah Khalid and Ahlawat, Timur Rampalsingh and Sankanur, Mahantappa Sangappa and Azreen Azman and Agrawal, Anuradha and Ghorpade, Prerna and Amirul Azuani Romle. (2024) Individual buffalo identification through muzzle dermatoglyphics images using deep learning approaches. Journal of Advanced Research in Applied Sciences and Engineering Technology, 59 (2). pp. 178-191. ISSN 2462-1943

Text 42a.pdf Restricted to Repository staff only Download (852kB)

Abstract

Animal identification is essential for routine farm operations, residue traceback, insurance, and ownership management. Owing to their uniqueness, incorrigible nature, tamperproof over time, environment-friendly, and pain-free, visual biometrics-based animal identification has recently gained momentum over traditional animal identification methods. Among visual biometrics-based cues, muzzle identification is a simple and relatively low-cost method. Therefore, to address the inherent significant limitations of conventional animal identification systems, we undertook this investigation to collect a database of digital images of muzzles that works as a benchmark, to apply deep learning frameworks to identify individual buffaloes from their muzzle images, and to compare their accuracy in terms of their identification capabilities. Muzzle images of 198 Surti buffaloes were subjected to transfer learning and fine-tuning processes in deep-learning neural networks. The performance was recorded for each pre-train model (ResNet50, InceptionV3, VGG16, AlexNet) with different hyperparameters of the epoch, batch size, and learning rate. A perusal of the data revealed that ResNet50 has the highest train accuracy (99.8%) and test accuracy (99.69%) among all four models used. AlexNet has the lowest train accuracy (90.8%) among the models. The findings concluded that all these four models could be applied to identify individual buffaloes; however, ResNet50 had the highest accuracy, and deep learning applications have great potential for individual buffalo identification and are promising tools for precision livestock farming.

Actions (login required)

View Item