Foundations of Livestock Behavioral Recognition: Ethogram Analysis of Behavioral Definitions and Its Practices in Multimodal Large Language Models

Computer vision offers a promising approach to automating the observation of animal behavior, thereby contributing to improved animal welfare and precision livestock management. However, the absence of standardized behavioral definitions limits the accuracy and generalizability of artificial intelligence models used for behavior recognition. This study applied natural language processing techniques to analyze 655 behavior definitions related to feeding, drinking, resting, and moving, as reported in the livestock research literature published between 2000 and 2023. Clustering and structural analyses revealed consistent semantic patterns across behavior categories. Feeding and drinking behaviors were concisely defined in 6-10 words, including the semantic elements of body parts, actions, and action objects. Resting and moving behaviors were described in 6-15 words. Resting behavior was defined by actions and action objects, while moving behaviors were characterized by action words only. By integrating these structured definitions into prompts, ChatGPT-4o achieved an average correspondence score of 4.53 out of 5 in an image-based piglet behavior annotation task. These findings highlight the value of standardized behavior definitions in supporting more accurate and generalizable behavior recognition models for precision livestock farming.