Search Results (786)

Animal-based measures, such as detecting inflammation in areas like the tail, ears, teats, coronary band, heels and claws (Swine Inflammation and Necrosis Syndrome, SINS), are used to monitor animal health and welfare. When parameters deviate from the established range, these measures enable prompt action to adjust husbandry practices, feeding regimens and management strategies. In addition to environmental factors, genetics have been shown to play a key role in inflammation and necrosis processes, and selection can reduce the severity of the disease. This study examined whether different breeds of AI boar exhibit different signs of SINS and how these signs are associated with SINS in their offspring when they are suckling piglets and weaners. Initially, 286 AI boars of 7 breeds from a German artificial insemination center were evaluated for SINS. The following parameters were assessed: tail base, tail tip, ears, skin, scrotum, coronary bands, heels and claws. Subsequently, 23 Pietrain and Duroc boars were used in combination with a Topigs DL sow line. The progeny of the AI boars was evaluated as suckling and weaned piglets, with the assessment framework encompassing SINS traits. The results revealed significant differences between the breeds and lines, as well as a strong correlation between the SINS phenotypes of the AI boars and the SINS scores of their offspring. The offspring of the 25% most extreme boars exhibited a 17% variation in SINS scores. This association was particularly evident when comparing the boars’ tail base. However, the development of the boars’ heels and claws was found to be significantly influenced by mechanical environmental factors and not associated with the piglets’ scores. These findings imply that heritable, endogenous processes, as proposed for SINS, also visibly impact the phenotype of the AI boar. This study’s fundamental premise suggests that pre-selecting AI boars could mitigate the occurrence of SINS and enhance piglet health and welfare. Full article

The poultry red mite (PRM) feeds on hens’ blood at night, disrupting sleep, harming welfare, and reducing productivity. Effective control may lie in dynamic Integrated Pest Management (IPM), which relies on routine monitoring and adaptation to farm conditions. This study investigated how PRM infestations affect the night-time activity of hens. Three groups of eight hens, housed in enriched cages, were monitored with night-vision cameras over a two-month period, both before and after artificial PRM introduction, while PRM levels were simultaneously recorded. To quantify changes in behaviour, we developed an activity-monitoring algorithm that extracts both group-level and individual night-time activity patterns from video recordings. Group activity between 18:00 and 03:00 was analyzed hourly, and individual activity between 21:00 and 00:00 was classified into four activity categories. Before infestation, group activity declined after 19:00, remained low from 20:00 to 01:00, and peaked just before the end of the dark period. After infestation, activity remained elevated with no anticipatory activity peak towards the end of the dark period. Individual data showed an increase in time spent in the most active activity category from 24% to 67% after infestation. The rise in calculated activity was supported by a nearly 23-fold increase in annotated PRM-related behaviours, specifically head shaking and head scratching. These findings suggest that PRM mostly disrupted sleep from two hours after lights-off to two hours before lights-on and may have acted as a chronic stressor. Automated video-based monitoring could strengthen dynamic IPM in commercial systems. Full article

This study addresses the growing need for intelligent monitoring in aquaponic systems by developing a predictive system based on artificial intelligence and environmental sensing. The goal is to improve fish welfare through the early detection of adverse water conditions. The system integrates low-cost digital sensors to continuously measure key physicochemical variables—pH, dissolved oxygen, and temperature—using these as inputs for real-time classification of fish health status. Four supervised machine learning models were evaluated: linear discriminant analysis (LDA), support vector machines (SVMs), neural networks (NNs), and random forest (RF). A dataset of 1823 instances was collected over eight months from a red tilapia aquaponic setup. The random forest model yielded the highest classification accuracy (99%), followed by NN (98%) and SVM (97%). LDA achieved 82% accuracy. Performance was validated using 5-fold cross-validation and label permutation tests to confirm model robustness. These results demonstrate that sensor-based predictive models can reliably detect early signs of fish stress or mortality, supporting the implementation of intelligent environmental monitoring and automation strategies in sustainable aquaponic production. Full article

Intestinal microbiota populations are constantly shaped by both intrinsic and extrinsic factors, including diet, environment, and host genetics. As a result, understanding how to assess, monitor, and exploit microbiome–host interplay remains an active area of investigation, especially in aquaculture. In this study, we analyzed the taxonomic structure and functional potential of the intestinal microbiota of European sea bass and rainbow trout, incorporating gilthead sea bream as a final reference. The results showed that the identified core microbiota (40 taxa for sea bass and 20 for trout) held a central role in community organization, despite taxonomic variability, and exhibited a predominant number of positive connections (>60% for both species) with the rest of the microbial community in a Bayesian network. From a functional perspective, core-associated bacterial clusters (75% for sea bass and 81% for sea bream) accounted for the majority of predicted metabolic pathways (core contribution: >75% in sea bass and >87% in trout), particularly those involved in carbohydrate, amino acid, and vitamin metabolism. Comparative analysis across ecological phenotypes highlighted distinct microbial biomarkers, with genera such as Vibrio, Pseudoalteromonas, and Paracoccus enriched in saltwater species (Dicentrarchus labrax and Sparus aurata) and Mycoplasma and Clostridium in freshwater (Oncorhynchus mykiss). Overall, this study underscores the value of integrating taxonomic, functional, and network-based approaches as practical tools to monitor intestinal health status, assess welfare, and guide the development of more sustainable production strategies in aquaculture. Full article

The daily behavioral patterns of giraffes reflect their health status and well-being. Behaviors such as licking, walking, standing, and eating are not only essential components of giraffes’ routine activities but also serve as potential indicators of their mental and physiological conditions. This is particularly relevant in captive environments such as zoos, where certain repetitive behaviors may signal underlying well-being concerns. Therefore, developing an efficient and accurate automated behavior detection system is of great importance for scientific management and welfare improvement. This study proposes a multi-behavior automatic detection method for giraffes based on YOLO11-Pose and the spatial-adaptive two-stream network (SATSN). Firstly, YOLO11-Pose is employed to detect giraffes and estimate the keypoints of their mouths. Observation-Centric SORT (OC-SORT) is then used to track individual giraffes across frames, ensuring temporal identity consistency based on the keypoint positions estimated by YOLO11-Pose. In the SATSN, we propose a region-of-interest extraction strategy for licking behavior to extract local motion features and perform daily behavior classification. In this network, the original 3D ResNet backbone in the slow pathway is replaced with a video transformer encoder to enhance global spatiotemporal modeling, while a Temporal Attention (TA) module is embedded in the fast pathway to improve the representation of fast motion features. To validate the effectiveness of the proposed method, a giraffe behavior dataset consisting of 420 video clips (10 s per clip) was constructed, with 336 clips used for training and 84 for validation. Experimental results show that for the detection tasks of licking, walking, standing, and eating behaviors, the proposed method achieves a mean average precision (mAP) of 93.99%. This demonstrates the strong detection performance and generalization capability of the approach, providing robust support for automated multi-behavior detection and well-being assessment of giraffes. It also lays a technical foundation for building intelligent behavioral monitoring systems in zoos. Full article

Neuromuscular blocking agents (NMBAs) are essential in intraocular surgeries to improve surgical conditions and ensure optimal ventilation. However, residual blockade can pose significant risks, particularly in pinnipeds due to their unique diving physiology. This case report describes the use of sugammadex for reversing rocuronium and AMG for monitoring neuromuscular block (NMB) in a California sea lion undergoing lensectomy. The objective is to evaluate the feasibility and safety of sugammadex for reversal of rocuronium-induced neuromuscular blockade and acceleromyography (AMG) for monitoring neuromuscular function in pinnipeds, with the goal of improving anesthetic management and recovery. Rocuronium (0.3 mg/kg IV) was used to achieve complete NMB, and an additional 0.1 mg/kg IV was administered to prolong the block. Sugammadex (1 mg/kg IV) reversed the NMB, with recovery within 90 s. Neuromuscular function was monitored using AMG, with the ulnar nerve of the foreflipper as the stimulation site. AMG allowed for an objective assessment of neuromuscular function, ensuring accurate titration of the NMBA and reversal agent. This is the first report documenting the use of sugammadex for the reversal of rocuronium and AMG for neuromuscular monitoring in a sea lion. This successful application highlights the potential of these techniques to improve anesthesia protocols, patient safety, and welfare in marine mammal medicine. Full article

Amphibian welfare, particularly pain assessment in anurans, remains understudied despite their ecological and biomedical significance. This study aimed to identify and validate operational pain indicators for adult anurans under professional care. A four-phase approach was used: a systematic literature review, expert validation with risk analysis, field validation in a zoological facility, and development of a preliminary pain index. From 158 publications, 16 potential indicators were identified, encompassing behavioural, clinical, and physiological signs. Expert evaluation by 28 professionals from 12 institutions refined this to seven indicators, achieving over 60% consensus: feeding behaviour changes, abnormal behaviour, impaired locomotion, oedema, reduced movement, retained skin post-moulting, and altered respiration. Field validation in 53 anurans confirmed high observability and ease of measurement, with feeding behaviour changes and oedema scoring highest for practicality (93.5% and 93.0%, respectively). These validated indicators provide a science-based foundation for routine welfare monitoring, enabling timely interventions. Their integration into husbandry protocols can enhance ethical standards, improve conservation outcomes, and increase public confidence in amphibian care, paving the way for a standardised anuran pain index. Full article

Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts. Full article

The aim of this project was to co-create an animal welfare monitoring system that incorporated both positive and negative welfare measures that would contribute to best practice husbandry standards of farm animals in a real animal research setting. Researchers worked with nine staff to co-design six bespoke welfare assessment protocols to be conducted in addition to legally required welfare monitoring for adult cattle, calves, sheep, pigs, and goats in specific experimental environments. Four protocols were subsequently applied with variable frequency by three staff to cattle, goats, and two pig populations. Assessments were all observational, and included behavioural scan sampling, Qualitative Behaviour Assessment scores, visual analogue mood scores, and physical condition data. Two staff provided feedback on their views of the process. A key finding was that with facilitation, staff could generate protocols that included elements designed to encourage or evaluate interventions to promote positive emotions. However, data collection was sporadic, and although the staff who provided feedback reported that they valued the process highly, they noted that the primary challenge was finding the time to conduct the assessments. We therefore conclude that sustained support is likely to be required to maximise the benefits for the animals and staff of developing and conducting voluntary welfare monitoring of farm animals. Full article

Non-contact, automated health monitoring is a cornerstone of modern precision livestock farming, crucial for enhancing animal welfare and productivity. Infrared thermography (IRT) offers a powerful, non-invasive means to assess physiological status. However, its practical use on farms is limited by a key challenge: accurately locating regions of interest (ROIs), like the eyes and face, in the blurry, low-resolution thermal images common in farm settings. To solve this, we developed a new framework called ID-APM, which is designed for robust ROI registration in agriculture. Our method uses a trinocular system and our RAP-CPD algorithm to robustly match features and accurately calculate the target’s 3D position. This 3D information then enables the precise projection of the ROI’s location onto the ambiguous thermal image through inverse disparity estimation, effectively overcoming errors caused by image blur and spectral inconsistencies. Validated on a self-built dataset of farmed sika deer, the ID-APM framework demonstrated exceptional performance. It achieved a remarkable overall accuracy of 96.95% and a Correct Matching Ratio (CMR) of 99.93%. This research provides a robust and automated solution that effectively bypasses the limitations of low-resolution thermal sensors, offering a promising and practical tool for precision health monitoring, early disease detection, and enhanced management of semi-wild farmed animals like sika deer. Full article

Shaded resting zones in rotational grazing systems are prone to thermal stress due to limited ventilation and the congregation of animals during peak heat periods. Addressing these challenges requires sensing solutions that are not only accurate but also capable of adapting to dynamic environmental conditions and energy constraints. In this context, we present the development and simulation-based validation of a self-configurable IoT protocol for adaptive environmental monitoring. The approach integrates embedded machine learning, specifically a Random Forest classifier, to detect critical conditions using synthetic data of temperature, humidity, and CO₂. The model achieved an accuracy of 98%, with a precision of 98%, recall of 85%, and F1-score of 91% in identifying critical states. These results demonstrate the feasibility of embedding adaptive intelligence into IoT-based monitoring solutions. The protocol is conceived as a foundation for integration into physical devices and subsequent evaluation in farm environments such as rotational grazing systems. Full article

The object of the research is the process of intelligent monitoring and diagnosis of animal health using IoT technology in the context of a digital farm. The problem lies in the absence of an integrated approach that can provide near-real-time assessment of an animal’s physiological and behavioral state, predict potential health risks, and adapt decision-making algorithms to specific species and environmental conditions. Traditional monitoring methods rely heavily on periodic manual inspection and limited sensor data, which reduces the timeliness and accuracy of diagnostics, especially for large-scale farms. To address this issue, a comprehensive model is proposed that integrates an IoT-based tag device for livestock, a data collection and transmission system, and an intelligent analysis module. The system utilizes statistical profiling to create baseline health parameters for each animal, applies anomaly detection methods to identify deviations, and leverages machine learning algorithms to predict health deterioration. The novelty of the approach lies in the combination of individualized baseline modeling, continuous sensor-based monitoring, and adaptive decision-making for early intervention. The approach scales across farm sizes and multi-sensor setups, making it practical for precision livestock farming. From a sustainability perspective, the approach enables earlier and more targeted interventions that can reduce unnecessary treatments, avoid preventable productivity losses, and support animal welfare. The design uses energy-aware IoT practices (on-device 60 s aggregation with one-minute uplinks) and lightweight analytics to limit device power use and network load, aligning the system with resource-efficient livestock operations. Full article

Background/Objectives: As global aging accelerates, there is a pressing and empirically substantiated demand for integrated and sustainable strategies, as evidenced by the rising prevalence rates of chronic conditions, social isolation, and digital exclusion among older adults worldwide. These factors underscore the urgent need for multidimensional interventions that simultaneously target physical, psychological, and social well-being. The HOPE-FIT (Hybrid Outreach Program for Exercise and Follow-up Integrated Training) model and the SAGE (Senior Active Guided Exercise) program were designed to address this need through a hybrid framework. These programs foster inclusive aging by explicitly bridging digitally underserved groups and mobility-restricted populations into mainstream health promotion systems through tailored exercise, psychosocial support, and smart-home technologies, thereby functioning as a scalable meta-model across healthcare, community, and policy domains. Methods: HOPE-FIT was developed through a formative, multi-phase process grounded in the RE-AIM framework and a Hybrid Type II effectiveness–implementation design. The program combines professional health coaching, home-based and digital exercise routines, Acceptance and Commitment Performance Training (ACPT)-based psychological strategies, and smart-home monitoring technologies. Empirical data from pilot studies, large-scale surveys (N = 1000), and in-depth user evaluations were incorporated to strengthen validity and contextual adaptation. Culturally tailored content and participatory feedback from older adults further informed ecological validity and program refinement. Implementation Strategy/Framework: The theoretical foundation integrates implementation science with behavioral and digital health. The RE-AIM framework guided reach, fidelity, and maintenance planning, while the Hybrid E–I design enabled the concurrent evaluation of effectiveness outcomes and contextual implementation strategies. Institutional partnerships with community centers, public health organizations, and welfare agencies further facilitated the translation of the model into real-world aging contexts. Dissemination Plan: The multi-pronged dissemination strategy includes international symposia, interdisciplinary academic networks, policy briefs, localized community deployment, and secure, authenticated data sharing for reproducibility. This design facilitates evidence-informed policy, empowers practitioners, and advances digital health equity. Ultimately, HOPE-FIT constitutes a scalable and inclusive model that concretely addresses health disparities and promotes active, dignified aging across systems and disciplines. Full article

Animal Welfare has a significant impact on the dairy cow’s health, behaviour, productivity and milk quality. By implementing husbandry practices that respect the physical, behavioural and emotional needs of dairy cows, the dairy industry can improve the sustainability of its operations and meet rising expectations. The aim of this study is to evaluate the impact of housing systems (free vs. tied) on dairy cow health through musculoskeletal health indicators and lameness scores. The hypothesis that dairy cows reared in free housing have a better quality of health than cows reared in restrained housing is tested. Thus, 300 dairy cows of the Holstein and Montbeliarde breeds were selected from dairy farms in five municipalities of Constantine province (eastern Algeria). The results showed that the frequency of severe lameness did not exceed 12% in stalls with restraints and more than 42% of light lameness are in free-stall housing (p < 0.001). These results reflect a lack of comfort in restricted housing, with an impact on dairy performances. Moreover, the monitoring of lame cows and the functional trimming of their hooves should be frequent. It is also important to implement a cull policy for unproductive cows. Finally, it is very important to provide adequate training to farmers in order to improve the well-being of dairy cows. Full article

Open-mouth panting in broiler chickens is a visible and critical indicator of heat stress and compromised welfare. However, detecting this behavior in densely populated cages is challenging due to the small size of the target and frequent occlusions and cluttered backgrounds. To overcome these issues, we proposed an enhanced object detection method based on the lightweight YOLOv8n framework, incorporating four key improvements. First, we add a dedicated P2 detection head to improve the recognition of small targets. Second, a space-to-depth grouped convolution module (SGConv) is introduced to capture fine-grained texture and edge features crucial for panting identification. Third, a bidirectional feature pyramid network (BIFPN) merges multi-scale feature maps for richer representations. Finally, a squeeze-and-excitation (SE) channel attention mechanism emphasizes mouth-related cues while suppressing irrelevant background noise. We trained and evaluated the method on a comprehensive, full-cycle broiler panting dataset covering all growth stages. Experimental results show that our method significantly outperforms baseline YOLO models, achieving 0.92 mAP@50 (independent test set) and 0.927 mAP@50 (leakage-free retraining), confirming strong generalizability while maintaining real-time performance. The initial evaluation had data partitioning limitations; method generalizability is now dually validated through both independent testing and rigorous split-then-augment retraining. This approach provides a practical tool for intelligent broiler welfare monitoring and heat stress management, contributing to improved environmental control and animal well-being. Full article