Multimodal AI Systems for Enhanced Laying Hen Welfare Assessment and Productivity Optimization

TL;DR

This paper explores multimodal AI systems that integrate visual, acoustic, environmental, and physiological data to improve welfare assessment and productivity in laying hens, addressing current limitations of traditional, subjective methods.

Contribution

It introduces a modular, scalable framework with novel evaluation tools for deploying multimodal AI in poultry farms, enhancing welfare monitoring and data reliability.

Findings

Intermediate fusion strategies outperform early and late fusion in robustness and scalability.

The Domain Transfer Score (DTS) effectively measures model adaptability across farms.

The Data Reliability Index (DRI) assesses sensor data quality in operational environments.

Abstract

The future of poultry production depends on a paradigm shift replacing subjective, labor-intensive welfare checks with data-driven, intelligent monitoring ecosystems. Traditional welfare assessments-limited by human observation and single-sensor data-cannot fully capture the complex, multidimensional nature of laying hen welfare in modern farms. Multimodal Artificial Intelligence (AI) offers a breakthrough, integrating visual, acoustic, environmental, and physiological data streams to reveal deeper insights into avian welfare dynamics. This investigation highlights multimodal As transformative potential, showing that intermediate (feature-level) fusion strategies achieve the best balance between robustness and performance under real-world poultry conditions, and offer greater scalability than early or late fusion approaches. Key adoption barriers include sensor fragility in harsh farm environments, high deployment costs, inconsistent behavioral definitions, and limited cross-farm generalizability. To address these, we introduce two novel evaluation tools - the Domain Transfer Score (DTS) to measure model adaptability across diverse farm settings, and the Data Reliability Index (DRI) to assess sensor data quality under operational constraints. We also propose a modular, context-aware deployment framework designed for laying hen environments, enabling scalable and practical integration of multimodal sensing. This work lays the foundation for a transition from reactive, unimodal monitoring to proactive, precision-driven welfare systems that unite productivity with ethical, science based animal care.