A Resource-Efficient Deep Learning Approach to Visual-Based Cattle Geographic Origin Prediction

Abstract

Customized healthcare for cattle health monitoring is essential, which aims to optimize individual animal health, thereby enhancing productivity, minimizing illness-related risks, and improving overall welfare. Tailoring healthcare practices to individual requirements guarantees that individual animals receive proper attention and intervention, resulting in better health outcomes and sustainable cattle farming practices. In this regard, the manuscript proposes a visual cues-based region prediction methodology to design a customized cattle healthcare system. The proposed automated AI healthcare system uses resource-efficient deep learning-inspired architecture for computer vision applications like performing region-wise classification. The classification mechanism can be used further to identify a cattle and the regions it belongs. Extensive experimentation has been conducted on a redesigned image dataset to identify the best-suited deep-learning framework to perform region classification for livestock, such as cattle. MobileNetV2 outperforms the considered state-of-the-art frameworks by achieving an accuracy of 93% in identifying the regions of the cattle. � The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.