Poultry Farm Intelligence: An Integrated Multi-Sensor AI Platform for Enhanced Welfare and Productivity

• URL: http://arxiv.org/abs/2510.15757v1
• Date: Fri, 17 Oct 2025 15:43:02 GMT
• Title: Poultry Farm Intelligence: An Integrated Multi-Sensor AI Platform for Enhanced Welfare and Productivity
• Authors: Pieris Panagi, Savvas Karatsiolis, Kyriacos Mosphilis, Nicholas Hadjisavvas, Andreas Kamilaris, Nicolas Nicolaou, Efstathios Stavrakis, Vassilis Vassiliades,
• Abstract summary: Poultry farming faces increasing pressure to meet productivity targets while ensuring animal welfare and environmental compliance.<n>Yet many small and medium-sized farms lack affordable, integrated tools for continuous monitoring and decision-making.<n>This paper presents Poultry Farm Intelligence (PoultryFI) - a modular, cost-effective platform that integrates six AI-powered modules.
• Score: 1.534601771389495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Poultry farming faces increasing pressure to meet productivity targets while ensuring animal welfare and environmental compliance. Yet many small and medium-sized farms lack affordable, integrated tools for continuous monitoring and decision-making, relying instead on manual, reactive inspections. This paper presents Poultry Farm Intelligence (PoultryFI) - a modular, cost-effective platform that integrates six AI-powered modules: Camera Placement Optimizer, Audio-Visual Monitoring, Analytics & Alerting, Real-Time Egg Counting, Production & Profitability Forecasting, and a Recommendation Module. Camera layouts are first optimized offline using evolutionary algorithms for full poultry house coverage with minimal hardware. The Audio-Visual Monitoring module extracts welfare indicators from synchronized video, audio, and feeding data. Analytics & Alerting produces daily summaries and real-time notifications, while Real-Time Egg Counting uses an edge vision model to automate production tracking. Forecasting models predict egg yield and feed consumption up to 10 days in advance, and the Recommendation Module integrates forecasts with weather data to guide environmental and operational adjustments. This is among the first systems to combine low-cost sensing, edge analytics, and prescriptive AI to continuously monitor flocks, predict production, and optimize performance. Field trials demonstrate 100% egg-count accuracy on Raspberry Pi 5, robust anomaly detection, and reliable short-term forecasting. PoultryFI bridges the gap between isolated pilot tools and scalable, farm-wide intelligence, empowering producers to proactively safeguard welfare and profitability.

Related papers

• Unified Generation and Self-Verification for Vision-Language Models via Advantage Decoupled Preference Optimization [48.078132893679744]
  We propose a unified reinforcement learning framework that jointly learns answer generation and self-verification within a single policy.<n>ADPO achieves up to +34.1% higher verification AUC and -53.5% lower inference time, with significant gains of +2.8%/+1.4% accuracy on MathVista/MMMU, +1.9 cIoU on ReasonSeg, and +1.7%/+1.0% step success rate on AndroidControl/GUI Odyssey.
  arXiv  Detail & Related papers  (2026-01-04T11:09:33Z)
• Optimizing video analytics inference pipelines: a case study [3.4152678224558333]
  This paper presents a comprehensive case study on optimizing a poultry welfare monitoring system.<n>We introduce a set of optimizations, including multi-level parallelization, optimizing code with substituting CPU code with GPU-accelerated code, vectorized clustering, and memory-efficient post-processing.
  arXiv  Detail & Related papers  (2025-12-07T21:17:53Z)
• Lightweight Hopfield Neural Networks for Bioacoustic Detection and Call Monitoring of Captive Primates [0.0]
  We present a transparent, lightweight and fast-to-train associative memory AI model with Hopfield neural network architecture.<n>Adapted from a model developed to detect bat echolocation calls, this model monitors captive endangered black-and-white ruffed lemur Varecia variegata vocalisations.
  arXiv  Detail & Related papers  (2025-11-04T17:46:03Z)
• In-the-Flow Agentic System Optimization for Effective Planning and Tool Use [73.72524040856052]
  AgentFlow is a trainable, in-the-flow agentic framework that coordinates four modules (planner, executor, verifier, generator) through an evolving memory.<n>Flow-GRPO tackles long-horizon, sparse-reward credit assignment by converting multi-turn optimization into a sequence of tractable single-turn policy updates.<n>AgentFlow with a 7B-scale backbone outperforms top-performing baselines with average accuracy gains of 14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on scientific tasks.
  arXiv  Detail & Related papers  (2025-10-07T05:32:44Z)
• 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)
• Multimodal AI Systems for Enhanced Laying Hen Welfare Assessment and Productivity Optimization [0.0]
  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.
  arXiv  Detail & Related papers  (2025-08-11T05:17:16Z)
• A Secured Triad of IoT, Machine Learning, and Blockchain for Crop Forecasting in Agriculture [1.7307517738946756]
  Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels.<n>Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45% accuracy rate in predicting optimal crop types and yields.<n>This integrated approach promises significant advances in precision agriculture, making crop forecasting more accurate, secure, and user-friendly.
  arXiv  Detail & Related papers  (2025-05-02T11:40:13Z)
• Neural Conformal Control for Time Series Forecasting [54.96087475179419]
  We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments.<n>Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders.<n>We empirically demonstrate significant improvements in coverage and probabilistic accuracy, and find that our method is the only one that combines good calibration with consistency in prediction intervals.
  arXiv  Detail & Related papers  (2024-12-24T03:56:25Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Bandit Data-Driven Optimization [62.01362535014316]
  There are four major pain points that a machine learning pipeline must overcome in order to be useful in settings. We introduce bandit data-driven optimization, the first iterative prediction-prescription framework to address these pain points. We propose PROOF, a novel algorithm for this framework and formally prove that it has no-regret.
  arXiv  Detail & Related papers  (2020-08-26T17:50:49Z)
• Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach [68.40429597811071]
  The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets. A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.
  arXiv  Detail & Related papers  (2020-07-07T10:00:32Z)

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.