Computer Vision for Emotion Identification on Sheep Images
DOI:
https://doi.org/10.21439/jme.v9i1.133Keywords:
Computer Vision, EfficientNet, Emotion detection, Pre-trained model, ResNet101, ResNet152, ResNet50, VGG16, YOLOv8Abstract
The rising global demand for meat and dairy products has accelerated the expansion of livestock farming, underscoring the need for advanced technologies to ensure animal welfare and productivity. This research explores the potential of automated monitoring systems, leveraging depth sensors and time-of-flight cameras, to provide valuable insights into environmental conditions, nutrition, health, and productivity. These systems enable the early detection of abnormal behaviors in large-scale farming operations, paving the way for more effective management practices. The study emphasizes the importance of understanding animal needs and introduces advanced models, including EfficientNet, ResNet50, ResNet101, ResNet152, and VGG16, for emotion recognition in sheep. These models achieve impressive accuracy rates ranging from 88% to 93%, significantly enhancing the ability to detect and classify emotional states such as pain. This capability represents a vital component of precision livestock farming, a practice that integrates real-time data with machine learning to support informed decision-making, optimize yields, and mitigate risks. Furthermore, the research highlights methodologies for animal identification, body condition assessment, and pain estimation, showcasing the potential of sophisticated imaging and perception technologies to revolutionize livestock farming. By improving welfare and operational efficiency, these advancements offer a sustainable approach to addressing the growing challenges in modern agriculture.
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