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

• URL: http://arxiv.org/abs/2508.07628v1
• Date: Mon, 11 Aug 2025 05:17:16 GMT
• Title: Multimodal AI Systems for Enhanced Laying Hen Welfare Assessment and Productivity Optimization
• Authors: Daniel Essien, Suresh Neethirajan,
• Abstract summary: Future of poultry production depends on replacing subjective, labor-intensive welfare checks with data-driven, intelligent monitoring ecosystems.<n>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.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models [56.94569090844015]
  TokaMark is a structured benchmark to evaluate AI models on real experimental data collected from the Mega Ampere Spherical Tokamak (MAST)<n>TokaMark aims to accelerate progress in data-driven AI-based plasma modeling, contributing to the broader goal of achieving sustainable and stable fusion energy.
  arXiv  Detail & Related papers  (2026-02-05T16:49:44Z)
• Sustainable Materials Discovery in the Era of Artificial Intelligence [3.222363676081407]
  We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment.<n>The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization.
  arXiv  Detail & Related papers  (2026-01-29T10:42:44Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• An Explainable AI based approach for Monitoring Animal Health [0.2749898166276853]
  Monitoring cattle health and optimizing yield are key challenges faced by dairy farmers due to difficulties in tracking all animals on the farm.<n>This work aims to showcase modern data-driven farming practices based on explainable machine learning(ML) methods that explain the activity and behaviour of dairy cattle (cows)
  arXiv  Detail & Related papers  (2025-08-13T21:40:35Z)
• MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving [63.875372281596576]
  MetAdv is a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation.<n>It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments.<n>It enables real-time capture of physiological signals and behavioral feedback from drivers.
  arXiv  Detail & Related papers  (2025-08-04T03:07:54Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift [12.82447756078577]
  Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness.<n>Inspired by biological sensory systems, we advocate for adaptive sensing as a necessary and foundational shift.<n>We outline a roadmap for broadly integrating adaptive sensing into real-world applications spanning humanoid, healthcare, autonomous systems, agriculture, and environmental monitoring.
  arXiv  Detail & Related papers  (2025-07-10T14:50:32Z)
• SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion [4.824120664293887]
  We introduce SatelliteCalculator, the first vision foundation model for quantitative remote sensing inversion.<n>By leveraging physically defined index adapters, we automatically construct a large-scale dataset of over one million paired samples.<n> Experiments demonstrate that SatelliteCalculator achieves competitive accuracy across all tasks while significantly reducing inference cost.
  arXiv  Detail & Related papers  (2025-04-18T03:48:04Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [69.38041171537573]
  Water quality is foundational to environmental sustainability, ecosystem resilience, and public health.<n>Deep learning offers transformative potential for large-scale water quality prediction and scientific insights generation.<n>Their widespread adoption in high-stakes operational decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations [8.916036880001734]
  Existing research overlooks the fragile multi-sensor correlations in multi-agent settings.<n>AgentAlign is a real-world heterogeneous agent cross-modality feature alignment framework.<n>We present a novel V2XSet-noise dataset that simulates realistic sensor imperfections under diverse environmental conditions.
  arXiv  Detail & Related papers  (2024-12-09T01:51:18Z)
• Cooperative Resilience in Artificial Intelligence Multiagent Systems [2.0608564715600273]
  This paper proposes a clear definition of cooperative resilience' and a methodology for its quantitative measurement. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions.
  arXiv  Detail & Related papers  (2024-09-20T03:28:48Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy [68.7563053122698]
  We propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. We present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty.
  arXiv  Detail & Related papers  (2022-10-19T19:15:07Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.