Smart Agricultural Technology· 2025Q1· Review
Multimodal AI systems for enhanced laying hen welfare assessment and productivity optimization
- 19citations
- Q1SCImago
- 2025year
Short summary
Multimodal AI, fusing visual, acoustic, environmental, and physiological data, offers a breakthrough for data-driven laying hen welfare assessment, outperforming traditional methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Multimodal AI integrates visual, acoustic, environmental, and physiological data for comprehensive laying hen welfare monitoring.
- Intermediate (feature-level) fusion architectures provide the best balance of robustness and performance for poultry environments.
- Key barriers to adoption include sensor fragility, high deployment costs, inconsistent behavioral taxonomies, and poor cross-farm generalizability.
- New frameworks like the Domain Transfer Score (DTS) and Data Reliability Index (DRI) are proposed to address generalizability and data quality issues.
AI-generated from the title and abstract; the full text is not read.
Abstract
The future of poultry production hinges on a revolutionary paradigm shift: transforming subjective, labor-intensive welfare assessments into data-driven, intelligent monitoring systems. Traditional welfare evaluation methods—constrained by human limitations and unimodal sensor dependencies—fail to capture the intricate, multidimensional nature of laying hen welfare in modern commercial environments. Multimodal Artificial Intelligence (AI) emerges as the critical breakthrough technology, orchestrating sophisticated fusion of visual, acoustic, environmental, and physiological data streams to unlock unprecedented insights into avian welfare dynamics. This comprehensive review synthesizes 130 peer-reviewed studies, revealing multimodal AI's transformative potential in laying hen welfare monitoring. Through systematic analysis of fusion architectures, we demonstrate that intermediate (feature-level) fusion strategies achieve optimal robustness-performance equilibrium under real-world poultry conditions, delivering superior scalability compared to early or late fusion approaches. Our investigation exposes critical implementation barriers: sensor fragility in harsh environments, prohibitive deployment costs, inconsistent behavioral taxonomies, and limited cross-farm generalizability that collectively impede widespread adoption. To overcome these challenges, we introduce two pioneering evaluation frameworks: the Domain Transfer Score (DTS) quantifying model generalizability across diverse farm conditions, and the Data Reliability Index (DRI) assessing sensor data quality under operational constraints. Additionally, we propose a modular, context-aware deployment framework specifically engineered for laying hen environments, enabling scalable integration of multimodal sensing technologies. This review establishes the scientific foundation for transitioning from reactive, unimodal monitoring to proactive, multimodal welfare systems, ultimately catalyzing the evolution toward precision-driven, ethically conscious poultry production that harmonizes productivity with animal welfare imperatives.
The authors' abstract, as published at the source. Smart Agricultural Technology, 2025 · DOI ↗
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