A Comprehensive Survey on Livestock Behavior Detection: From Traditional and Sensor-Based Methods to Deep Learning Approaches

Abstract

Precise Livestock Farming (PLF) is reliant on basic Behavior diagnosis of livestock; since livestock's Behavior is well recognised for assessing their health, welfare and productivity. Manual observation and wearable sensor-based approaches are limited in scalability, subjectivity, ease of maintenance, and/or animal discomfort. The emergence of computer vision and deep learning nowadays enables continuous, automatic, and noninvasive monitoring of subjects' Behaviors using video data. A comprehensive literature review on livestock behavior detection methods, emphasizing deep learning approaches using visual data such as CNNs, recurrent neural networks, and transformer models, is conducted. Existing methods are assessed in terms of accuracy, robustness, scalability and real-time performance and publicly available datasets are described. Other critical topics, including Behavioral variation, environmental variability, and data quality in data annotation methods, are also addressed. Lastly, current research trends such as self-supervised learning techniques, multimodal fusion, synthetic data generation, and edge-efficient transformers are discussed to pave the way for future research directions in intelligent livestock monitoring systems.