The rapid growth of the global poultry industry, driven by rising demand for affordable animal protein, has intensified public discourse surrounding production practices, housing, management, animal welfare, and supply-chain transparency. Social media platforms such as X (formerly Twitter) generate large volumes of unstructured textual data that capture stakeholder sentiment across the poultry industry. Extracting accurate sentiment signals from this domain-specific discourse remains challenging due to contextual ambiguity, linguistic variability, and limited domain awareness in general-purpose language models. This study presents PoultryLeX-Net, a lexicon-enhanced, domain-adaptive dual-stream transformer framework for fine-grained sentiment analysis in poultry-related text. The proposed architecture integrates sentiment classification, topic modeling, and contextual representation learning through domain-specific embeddings and gated cross-attention mechanisms. A lexicon-guided stream captures poultry-specific terminology and sentiment cues, while contextual stream models long-range semantic dependencies. Latent Dirichlet Allocation is employed to identify dominant thematic struct
The Poultry industry plays a vital role in global food security, yet small- and medium-scale farmers frequently lack timely access to expert-level support for disease diagnosis, nutrition planning, and management decisions. With rising climate stress, unpredictable feed prices, and persistent disease threats, poultry producers often struggle to make quick, informed decisions. Therefore, there is a critical need for intelligent, data-driven systems that can deliver reliable, on-demand consultation. This paper presents PoultryTalk, a novel multi-modal Retrieval-Augmented Generation (RAG) system designed to provide real-time expert guidance through text and image-based interaction. PoultryTalk uses OpenAI's text-embedding-3-small and GPT-4o to provide smart, context-aware poultry management advice from text, images, or questions. System usability and performance were evaluated using 200 expert-verified queries and feedback from 34 participants who submitted 267 queries to the PoultryTalk prototype. The expert-verified benchmark queries confirmed strong technical performance, achieving a semantic similarity of 84.0% and an average response latency of 3.6 seconds. Compared with OpenAI's
Early detection of highly pathogenic avian influenza (HPAI) and endemic poultry diseases is critical for global food security. While computer vision models excel at classifying diseases from fecal imaging, deploying these systems at scale is bottlenecked by farm data privacy concerns and institutional data silos. Furthermore, existing open-source agricultural datasets frequently suffer from severe, undocumented data contamination. In this paper, we introduce $\textbf{FecalFed}$, a privacy-preserving federated learning framework for poultry disease classification. We first curate and release $\texttt{poultry-fecal-fl}$, a rigorously deduplicated dataset of 8,770 unique images across four disease classes, revealing and eliminating a 46.89$\%$ duplication rate in popular public repositories. To simulate realistic agricultural environments, we evaluate FecalFed under highly heterogeneous, non-IID conditions (Dirichlet $α=0.5$). While isolated single-farm training collapses under this data heterogeneity, yielding only 64.86$\%$ accuracy, our federated approach recovers performance without centralizing sensitive data. Specifically, utilizing server-side adaptive optimization (FedAdam) wi
Detecting and localizing poultry is essential for advancing smart poultry farming. Despite the progress of detection-centric methods, challenges persist in free-range settings due to multiscale targets, obstructions, and complex or dynamic backgrounds. To tackle these challenges, we introduce an innovative poultry detection approach named SFN-YOLO that utilizes scale-aware fusion. This approach combines detailed local features with broader global context to improve detection in intricate environments. Furthermore, we have developed a new expansive dataset (M-SCOPE) tailored for varied free-range conditions. Comprehensive experiments demonstrate our model achieves an mAP of 80.7% with just 7.2M parameters, which is 35.1% fewer than the benchmark, while retaining strong generalization capability across different domains. The efficient and real-time detection capabilities of SFN-YOLO support automated smart poultry farming.
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 op
Poultry farming is a vital component of the global food supply chain, yet it remains highly vulnerable to infectious diseases such as coccidiosis, salmonellosis, and Newcastle disease. This study proposes a lightweight machine learning-based approach to detect these diseases by analyzing poultry fecal images. We utilize multi-color space feature extraction (RGB, HSV, LAB) and explore a wide range of color, texture, and shape-based descriptors, including color histograms, local binary patterns (LBP), wavelet transforms, and edge detectors. Through a systematic ablation study and dimensionality reduction using PCA and XGBoost feature selection, we identify a compact global feature set that balances accuracy and computational efficiency. An artificial neural network (ANN) classifier trained on these features achieved 95.85% accuracy while requiring no GPU and only 638 seconds of execution time in Google Colab. Compared to deep learning models such as Xception and MobileNetV3, our proposed model offers comparable accuracy with drastically lower resource usage. This work demonstrates a cost-effective, interpretable, and scalable alternative to deep learning for real-time poultry disease
Existing XAI metrics measure faithfulness for a single model, ignoring model multiplicity where near-optimal classifiers rely on different or spurious acoustic cues. In noisy farm environments, stationary artifacts such as ventilation noise can produce explanations that are faithful yet unreliable, as masking-based metrics fail to penalize redundant shortcuts. We propose AGRI-Fidelity, a reliability-oriented evaluation framework for listenable explanations in poultry disease detection without spatial ground truth. The method combines cross-model consensus with cyclic temporal permutation to construct null distributions and compute a False Discovery Rate (FDR), suppressing stationary artifacts while preserving time-localized bioacoustic markers. Across real and controlled datasets, AGRI-Fidelity effectively provides reliability-aware discrimination for all data points versus masking-based metrics.
Automated monitoring of animal welfare has largely targeted negative indicators, leaving positive welfare behaviours such as play underexplored. To address this gap, we present PlayClass, a pipeline for play-behaviour classification in poultry from top-down pen video. The pipeline leverages long-duration tracking with SAM 3 via YOLO-guided chunk boundaries to minimise identity errors in point-based prompting, and frozen embeddings from image and video foundation models for play action classification. Although handcrafted motion features from tracked masks alone achieved competitive accuracy, V-JEPA 2.1 consistently outperformed all other backbones across model scales, reaching 77.0 macro-averaged F$_1$ when combined with handcrafted features. Despite this result, the dataset remains challenging due to play sub-types sharing similar kinematic profiles with non-play and inter-bird occlusion. Overall, our work provides encouraging evidence towards automated frameworks for play behaviour classification in poultry.
Indoor poultry farms require inspection robots to maintain precise environmental control, which is crucial for preventing the rapid spread of disease and large-scale bird mortality. However, the complex conditions within these facilities, characterized by areas of intense illumination and water accumulation, pose significant challenges. Traditional navigation methods that rely on a single sensor often perform poorly in such environments, resulting in issues like laser drift and inaccuracies in visual navigation line extraction. To overcome these limitations, we propose a novel composite navigation method that integrates both laser and vision technologies. This approach dynamically computes a fused yaw angle based on the real-time reliability of each sensor modality, thereby eliminating the need for physical navigation lines. Experimental validation in actual poultry house environments demonstrates that our method not only resolves the inherent drawbacks of single-sensor systems, but also significantly enhances navigation precision and operational efficiency. As such, it presents a promising solution for improving the performance of inspection robots in complex indoor poultry farmin
Accurate mapping of column-averaged CO2 (XCO2) over agricultural landscapes is essential for guiding emission mitigation strategies. We present a Spatiotemporal Vision Transformer with Wavelets (ST-ViWT) framework that reconstructs continuous, uncertainty-quantified XCO2 fields from OCO-2 across southern Canada, emphasizing poultry-intensive regions. The model fuses wavelet time-frequency representations with transformer attention over meteorology, vegetation indices, topography, and land cover. On 2024 OCO-2 data, ST-ViWT attains R2 = 0.984 and RMSE = 0.468 ppm; 92.3 percent of gap-filled predictions lie within +/-1 ppm. Independent validation with TCCON shows robust generalization (bias = -0.14 ppm; r = 0.928), including faithful reproduction of the late-summer drawdown. Spatial analysis across 14 poultry regions reveals a moderate positive association between facility density and XCO2 (r = 0.43); high-density areas exhibit larger seasonal amplitudes (9.57 ppm) and enhanced summer variability. Compared with conventional interpolation and standard machine-learning baselines, ST-ViWT yields seamless 0.25 degree CO2 surfaces with explicit uncertainties, enabling year-round coverage
Detecting anomalies in poultry houses is crucial for maintaining optimal chicken health conditions, minimizing economic losses and bolstering profitability. This paper presents a novel real-time framework for analyzing chicken behavior in cage-free poultry houses to detect abnormal behaviors. Specifically, two significant abnormalities, namely inactive broiler and huddling behavior, are investigated in this study. The proposed framework comprises three key steps: (1) chicken detection utilizing a state-of-the-art deep learning model, (2) tracking individual chickens across consecutive frames with a fast tracker module, and (3) detecting abnormal behaviors within the video stream. Experimental studies are conducted to evaluate the efficacy of the proposed algorithm in accurately assessing chicken behavior. The results illustrate that our framework provides a precise and efficient solution for real-time anomaly detection, facilitating timely interventions to maintain chicken health and enhance overall productivity on poultry farms. Github: https://github.com/TaherehZarratEhsan/Chicken-Behavior-Analysis
In the poultry industry, detecting chicken illnesses is essential to avoid financial losses. Conventional techniques depend on manual observation, which is laborious and prone to mistakes. Using YOLO v8 a deep learning model for real-time object recognition. This study suggests an AI based approach, by developing a system that analyzes high resolution chicken photos, YOLO v8 detects signs of illness, such as abnormalities in behavior and appearance. A sizable, annotated dataset has been used to train the algorithm, which provides accurate real-time identification of infected chicken and prompt warnings to farm operators for prompt action. By facilitating early infection identification, eliminating the need for human inspection, and enhancing biosecurity in large-scale farms, this AI technology improves chicken health management. The real-time features of YOLO v8 provide a scalable and effective method for improving farm management techniques.
Woody breast (WB) is a myopathy in modern broiler chickens that causes the breast muscle to become unusually stiff and fibrous, leading to decreased meat quality and significant economic losses. State-of-the-art automated WB detection relies on a side-view imaging system to analyze the bending behavior of a single fillet as it falls off a conveyor belt. While highly accurate, this approach is constrained by its single-fillet field of view, creating throughput bottlenecks on commercial processing lines. In this paper, we address this limitation via a novel multi-fillet detection architecture utilizing a top-down camera configuration. To validate our approach, we first develop a high-fidelity digital twin of an industrial conveyor system. Next, we synthesize a diverse dataset of 3D fillet meshes and model their viscoelastic bending dynamics using a physics-based simulation engine. Lastly, a continuous 2D shape deformation score is extracted from the top-down perspective as the simulated fillets traverse the roller precipice. Experimental results demonstrate that the top-down shape score effectively captures the contour changes of the fillets as it bends, providing a robust and scalab
In the food industry, assessing the quality of poultry carcasses during processing is a crucial step. This study proposes an effective approach for automating the assessment of carcass quality without requiring skilled labor or inspector involvement. The proposed system is based on machine learning (ML) and computer vision (CV) techniques, enabling automated defect detection and carcass quality assessment. To this end, an end-to-end framework called CarcassFormer is introduced. It is built upon a Transformer-based architecture designed to effectively extract visual representations while simultaneously detecting, segmenting, and classifying poultry carcass defects. Our proposed framework is capable of analyzing imperfections resulting from production and transport welfare issues, as well as processing plant stunner, scalder, picker, and other equipment malfunctions. To benchmark the framework, a dataset of 7,321 images was initially acquired, which contained both single and multiple carcasses per image. In this study, the performance of the CarcassFormer system is compared with other state-of-the-art (SOTA) approaches for both classification, detection, and segmentation tasks. Throu
The rapid growth of artificial intelligence in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets. Manual annotation is time-consuming and costly, making it impractical for modern systems that continuously generate data. This study addresses this challenge by exploring semi-supervised auto-labeling methods, integrating self and active learning approaches to develop an efficient, label-scarce framework for auto-labeling large poultry datasets (ALPD). For this study, video data were collected from broilers and laying hens housed. Various machine learning models, including zero-shot models and supervised models, were utilized for broilers and hens detection. The results showed that YOLOv8s-World and YOLOv9s performed better when compared performance metrics for broiler and hen detection under supervised learning, while among the semi-supervised model, YOLOv8s-ALPD achieved the highest precision (96.1%) and recall (99%) with an RMSE of 1.87. The hybrid YOLO-World model, incorporating the optimal YOLOv8s backbone with zero-shot models, demonstrated the highest overall performance. It achieved a precision of 99.2%, recall of 99.4%, and an F1 sco
A survey was conducted from February 2022 to May 2022 on the usage of antibiotics at a poultry farm in different areas of Multan, Punjab Pakistan. A well-organized questionnaire was used for the collection of data. Sixty poultry farms were surveyed randomly in the Multan district. All of these Farms were using antibiotics. Antibiotics are commonly used for the treatment of diseases. Some are used as preventive medicine and a few are used as growth promotors. neomycin, erythromycin, oxytetracycline, streptomycin, and colistin are the broad-spectrum antibiotics that are being used commercially. Enrofloxacin and Furazolidone are the common antibiotics that are being used in Studies these days. The class of Fluoroquinolones is commonly used in poultry farms. Thirty-three patterns of antibiotic usage were observed at poultry farms. multi-drug practices were also observed on various farms. In this study, 25% of antibiotics are prescribed by the veterans while more than 90 % were acquired from the veterinary store. This study provides information about the antibiotics which are commonly being used in the study location district Multan. It is expected that the finding of this survey will b
Deciphering the acoustic language of chickens offers new opportunities in animal welfare and ecological informatics. Their subtle vocal signals encode health conditions, emotional states, and dynamic interactions within ecosystems. Understanding the semantics of these calls provides a valuable tool for interpreting their functional vocabulary and clarifying how each sound serves a specific purpose in social and environmental contexts. We apply advanced Natural Language Processing and transformer based models to translate bioacoustic data into meaningful insights. Our method integrates Wave2Vec 2.0 for raw audio feature extraction with a fine tuned Bidirectional Encoder Representations from Transformers model, pretrained on a broad corpus of animal sounds and adapted to poultry tasks. This pipeline decodes poultry vocalizations into interpretable categories including distress calls, feeding signals, and mating vocalizations, revealing emotional nuances often overlooked by conventional analyses. Achieving 92 percent accuracy in classifying key vocalization types, our approach demonstrates the feasibility of real time automated monitoring of flock health and stress. By tracking this f
Poultry farming plays a pivotal role in addressing human food demand. Robots are emerging as promising tools in poultry farming, with the potential to address sustainability issues while meeting the increasing production needs and demand for animal welfare. This review aims to identify the current advancements, limitations and future directions of development for robotics in poultry farming by examining existing challenges, solutions and innovative research, including robot-animal interactions. We cover the application of robots in different areas, from environmental monitoring to disease control, floor eggs collection and animal welfare. Robots not only demonstrate effective implementation on farms but also hold potential for ethological research on collective and social behaviour, which can in turn drive a better integration in industrial farming, with improved productivity and enhanced animal welfare.
This paper investigates the demand for lamb, beef, pork, and poultry in Canada, both at the national level and in disaggregated provinces, to identify meat consumption patterns in different provinces. Meat consumption plays a significant role in Canada's economy and is an important source of calories for the population. However, meat demand faces several consumption challenges due to logistic constraints, as a significant portion of the supply is imported from other countries. Therefore, there is a need for a better understanding of the causal relationships underlying lamb, beef, pork, and poultry consumption in Canada. Until recently, there have been no attempts to estimate meat consumption at the provincial level in Canada. Different Almost Ideal Demand System (AIDS) models have been applied for testing specifications to circumvent several econometric and theoretical problems. In particular, generalized AIDS and its Quadratic extension QUAIDS methods have been estimated across each province using the Iterative Linear Least Squares Estimator (ILLE) estimation Method. Weekly retail meat consumption price and quantity data from 2019 to 2022 have been used for Canada and for each pro
Based on World Health Organization (WHO) fact sheet in the 2011, outbreaks of poultry diseases especially Avian Influenza in poultry may raise global public health concerns due to their effect on poultry populations, their potential to cause serious disease in people, and their pandemic potential. In this research, we built a Poultry Diseases Expert System using Dempster-Shafer Theory. In this Poultry Diseases Expert System We describe five symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. The result of the research is that Poultry Diseases Expert System has been successfully identifying poultry diseases.