标题： 多模态人工智能系统用于提升产蛋鸡福利评估和生产性能优化 显示英文标题

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

摘要： 家禽生产的未来取决于范式转变，将主观、劳动密集型的福利检查替换为数据驱动的智能监测生态系统。 传统的福利评估受到人类观察和单一传感器数据的限制，无法完全捕捉现代农场中产蛋鸡福利的复杂多维特性。 多模态 人工智能（AI）提供了一个突破，整合视觉、听觉、环境和生理数据流，以揭示家禽福利动态的更深层次见解。 本研究强调了多模态 的变革潜力，表明中间（特征级）融合策略在现实中的家禽条件下实现了稳健性和性能之间的最佳平衡，并且比早期或晚期融合方法具有更大的可扩展性。 关键的采用障碍包括在恶劣农场环境中传感器的脆弱性、高部署成本、行为定义不一致以及跨农场的泛化能力有限。 为了解决这些问题，我们引入了两个新的评估工具——领域迁移评分（DTS），用于衡量模型在不同农场环境中的适应能力，以及数据可靠性指数（DRI），用于在操作约束下评估传感器数据质量。 我们还提出了一种模块化、上下文感知的部署框架，专为产蛋鸡环境设计，使多模态传感的可扩展和实用集成成为可能。 这项工作为从反应性、单模态监测向主动、精准驱动的福利系统转变奠定了基础，将生产率与伦理、基于科学的动物护理结合起来。 显示英文摘要

摘要： 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.