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

## TL;DR

This paper explores how standardizing behavior definitions improves AI accuracy in monitoring livestock behavior, using natural language processing and large language models.

## Contribution

The study introduces structured behavior definitions that enhance the consistency and accuracy of AI-based livestock behavior recognition.

## Key findings

- Feeding and drinking behaviors are best defined with concise descriptions including body parts, actions, and objects.
- Resting and moving behaviors show distinct linguistic patterns that can be used to guide AI models.
- Using structured definitions in prompts improved ChatGPT-4o's performance in annotating piglet behavior images.

## Abstract

Accurate behavior monitoring is critical for livestock health, welfare, and productivity. While computer vision is widely used for automated tracking, inconsistent behavior definitions across studies can reduce the reliability, consistency, and interpretability of data annotation and model performance. In this study, we used natural language processing (NLP) to examine 655 behavior definitions from research involving cattle, pigs, sheep, and horses. We identified common linguistic patterns in how behaviors are described. To assess the practical value of these patterns, we incorporated them into a behavior recognition task using a large language model (LLM). The results showed that the LLM performed more consistently and accurately when guided by these language patterns. Our findings suggest that improving the clarity and consistency of behavior definitions can enhance the reliability and interpretability of AI-based livestock behavior monitoring.

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.