Search Results (222)

Accurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation (CV), relative change (ROC), and absolute change (AC). In this study, kernel density estimation (KDE) is employed not as a predictive model, but as a distributional tool to robustly extract representative flock weight from noisy, high-frequency scale measurements under commercial farm conditions. In the absence of physical ground-truth, our evaluation focused on the framework’s ability to consistently detect the single, representative peak in the KDE distribution. Weekly thresholds were empirically optimized for the preprocessing filters. Results show that the combined ROC + AC method consistently produced unimodal peak distributions and improved the Peak Detection Rate (PDR) from 91.2% (raw data) to 97.9%. Single-Entity Filtering, assisted by cameras, further mitigated density distortions caused by prolonged occupancy, while CV-only and ROC-only filtering yielded less stable representative values. These findings demonstrate that rigorous preprocessing is essential for reliable KDE-based weight estimation under real-world farm conditions. The proposed framework not only improves data quality and stabilizes distributions but also provides a practical foundation for real-time monitoring and AI-driven precision livestock farming models. Full article

Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic measurement of body height, body length, and chest circumference from side-view images of sheep. The model employs a synergistic strategy that combines semantic segmentation with 3D point cloud geometric fitting. It incorporates the SegLinearSimAM feature enhancement module, the SEAttention channel optimization module, and the ENMPDIoU loss function to improve measurement robustness under complex backgrounds and occlusions. After segmentation, valid RGB-D point clouds are generated through depth completion and point cloud filtering, enabling 3D computation of key body measurements. Experimental results demonstrate that the improved model outperforms the baseline YOLOv12n-Seg: the mAP@0.5 for segmentation reaches 94.20%, the mAP@0.5 for detection reaches 95.00% (improvements of 0.5 and 1.3 percentage points, respectively), and the recall increases to 99.00%. In validation tests on 43 Hu sheep, the R² values for chest circumference, body height, and body length were 0.925, 0.888 and 0.819, respectively, with measurement errors within 5%. The model requires only 10.71 MB of memory and 9.9 GFLOPs of computation, enabling real-time operation on edge devices. This study demonstrates that the proposed method achieves non-contact automatic measurement of sheep body dimensions, providing a practical solution for on-site growth monitoring and intelligent management in livestock farms. Full article

Respiratory diseases remain one of the most significant health challenges in modern swine production, leading to substantial economic losses, compromised animal welfare, and increased antimicrobial use. In recent years, advances in artificial intelligence (AI), particularly machine learning and deep learning, have enabled the development of non-invasive, continuous monitoring systems based on pig vocalizations. Among these, audio-based technologies have emerged as especially promising tools for early detection and monitoring of respiratory disorders under real farm conditions. This review provides a comprehensive synthesis of AI-driven audio classification approaches applied to pig farming, with focus on respiratory health and welfare monitoring. First, the biological and acoustic foundations of pig vocalizations and their relevance to health and welfare assessment are outlined. The review then systematically examines sound acquisition technologies, feature engineering strategies, machine learning and deep learning models, and evaluation methodologies reported in the literature. Commercially available systems and recent advances in real-time, edge, and on-farm deployment are also discussed. Finally, key challenges related to data scarcity, generalization, environmental noise, and practical deployment are identified, and emerging opportunities for future research including multimodal sensing, standardized datasets, and explainable AI are highlighted. This review aims to provide researchers, engineers, and industry stakeholders with a consolidated reference to guide the development and adoption of robust AI-based acoustic monitoring systems for respiratory health management in swine. Full article

Communication in mammals constitutes a complex, multimodal system that integrates visual, acoustic, tactile, and chemical signals whose functions extend beyond simple information transfer to include the regulation of social relationships, coordination of behaviour, and expression of emotional states. This article examines the fundamental mechanisms of communication from biological, neuroethological, and behavioural perspectives, with particular emphasis on domesticated and farmed species. Analysis of sensory signals demonstrates that their perception and interpretation are closely linked to the physiology of sensory organs as well as to social experience and environmental context. In companion animals such as dogs and cats, domestication has significantly modified communicative repertoires ranging from the development of specialised facial musculature in dogs to adaptive diversification of vocalisations in cats. The neurobiological foundations of communication, including the activity of the amygdala, limbic structures, and mirror-neuron systems, provide evidence for homologous mechanisms of emotion recognition across species. The article also highlights the role of communication in shaping social structures and the influence of husbandry conditions on the behaviour of farm animals. In intensive production environments, acoustic, visual, and chemical signals are often shaped or distorted by crowding, noise, and chronic stress, with direct consequences for welfare. Furthermore, the growing importance of multimodal technologies such as Precision Livestock Farming (PLF) and Animal–Computer Interaction (ACI) is discussed, particularly their role in enabling objective monitoring of emotional states and behaviour and supporting individualised care. Overall, the analysis underscores that communication forms the foundation of social functioning in mammals, and that understanding this complexity is essential for ethology, animal welfare, training practices, and the design of modern technologies facilitating human–animal interaction. Full article

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing the effect of soil pixel removal. A comprehensive machine learning pipeline (12 algorithms and 6 feature selection methods) was applied to 14 data combinations. Our results demonstrated that soil removal consistently improved the performance of the applied models. Multispectral (MSI) sensors were the most robust individually, whereas textural (GLCM) attributes did not contribute significantly. Although the MSI and RGB data combination proved complementary, the model with the highest accuracy was obtained with CatBoost using only RGB information after Boruta feature selection, achieving a CCC of 0.83, RMSE of 0.214 kg, and R² of 0.81 in the test set. The most important variable was vegetation cover area (19.94%), surpassing spectral indices. We conclude that integrating RGB UAVs with robust processing can generate accessible and effective tools for forage monitoring. This approach can support pasture management by optimizing stocking rates, enhancing natural resource efficiency, and supporting data-driven decisions in precision silvopastoral systems. Full article

Automated livestock monitoring in wide-area grasslands is a critical component of smart agriculture development. Devices such as Unmanned Aerial Vehicles (UAVs), remote sensing, and high-mounted cameras provide unique monitoring perspectives for this purpose. The high-resolution images they capture cover vast grassland backgrounds, where targets often appear as small, distant objects and are extremely unevenly distributed. Applying standard detectors directly to such images yields poor results and extremely high miss rates. To improve the detection accuracy of small targets in high-resolution images, methods represented by Slicing Aided Hyper Inference (SAHI) have been widely adopted. However, in specific scenarios, SAHI’s drawbacks are dramatically amplified. Its strategy of uniform global slicing divides each original image into a fixed number of sub-images, many of which may be pure background (negative samples) containing no targets. This results in a significant waste of computational resources and a precipitous drop in inference speed, falling far short of practical application requirements. To resolve this conflict between accuracy and efficiency, this paper proposes an efficient detection framework named CAMS-AI (Clustering and Adaptive Multi-level Slicing for Aided Inference). CAMS-AI adopts a “coarse-to-fine” intelligent focusing strategy: First, a Region Proposal Network (RPN) is used to rapidly locate all potential target areas. Next, a clustering algorithm is employed to generate precise Regions of Interest (ROIs), effectively focusing computational resources on target-dense areas. Finally, an innovative multi-level slicing strategy and a high-precision model are applied only to these high-quality ROIs for fine-grained detection. Experimental results demonstrate that the CAMS-AI framework achieves a mean Average Precision (mAP) comparable to SAHI while significantly increasing inference speed. Taking the RT-DETR detector as an example, while achieving 96% of the mAP_50–95 accuracy level of the SAHI method, CAMS-AI’s end-to-end frames per second (FPS) is 10.3 times that of SAHI, showcasing its immense application potential in real-world, high-resolution monitoring scenarios. Full article

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems. Full article

Non-invasive and intelligent technologies have been utilized to monitor agricultural systems in real time, facilitating expedient decision-making and the reduction in animal stress in diverse climatic conditions. The objective of this study was to develop a deep learning supervised model to classify Nelore cattle (Bos indicus) into two groups: those in comfort and those under thermal stress. Thirty cattle, aged between 18 and 20 months, were evaluated between June and December 2023, resulting in 676 samples collected across four daily periods (6:00, 12:00, 18:00, and 24:00). Biotic variables included rectal temperature (RT) and respiratory rate (RR), while abiotic variables included air temperature (AT) and relative humidity (RH). The neural network model exhibited an accuracy and recall of 72% but a low specificity of 42%. These metrics indicate that while the model is effective in detecting stress situations, it faces challenges in correctly identifying animals in thermal comfort, likely due to class imbalance and the need for additional input features to capture environmental adaptability. Consequently, it can be posited that supervised learning models are valuable tools for precision livestock farming, provided that discriminatory limitations are mitigated by refining input characteristics and data balancing. Full article

Precision livestock farming (PLF), by integrating multimodal sensor data, provides opportunities to enhance welfare monitoring and management in small ruminants. This study evaluated whether environmental, physiological, and behavioral measurements—including the temperature–humidity index (THI), carbon dioxide (CO₂) and ammonia (NH₃) concentrations measured at the barn level, body condition score (BCS), rectal and ocular temperatures, GPS-derived locomotion metrics, accelerometry data, and fixed animal traits—can serve as key predictors of welfare and productivity in dairy sheep. Data were collected from 90 ewes: all animals underwent the same repeated welfare assessments, while 30 of them were additionally equipped with GPS–accelerometer sensor collars; environmental conditions were continuously recorded for the entire flock, generating 773 complete multimodal records. All predictive models were developed using data from all 90 ewes; collar-derived behavioral variables were included only for individuals equipped with GPS–accelerometer collars. Nine regression methods (linear regression (LR), partial least square regression (PLSR), elastic net (EN), mixed-effects models, random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), neural networks (multilayer perceptron, MLP), and an ensemble of RF–XGBoost–EN were evaluated using a combination of nested cross-validation (CV) and leave-one-animal-out CV (LOAOCV) to ensure robustness and generalization at the individual animal level. Nonlinear models—particularly RF, XGBoost, SVR, and the ensemble—consistently delivered superior performance across traits. For behavioral (e.g., daily distance movement) and thermal indicators (e.g., medial canthus temperature), the highest predictive capacity (R² ≈ 0.60–0.70) was achieved, while moderate predictive capacity (R² ≈ 0.40–0.50 and ≈0.35–0.45), respectively, was observed for respiratory rate and milk yield, reflecting their multifactorial nature. Feature importance analyses underscored the relevance of THI, CO₂, NH₃, concentrations, and BCS across results. Overall, these findings demonstrate that multimodal sensor fusion can effectively support the prediction of welfare and productivity indicators in intensively reared dairy sheep and emphasize the need for larger and more diverse datasets to further enhance model generalizability and model transferability. Full article

Morocco ranked 9th in the 2024 Climate Change Performance Index (CCPI), placing it among the world’s top 10 performers in climate action. Building on this leadership, our review outlines practical and real-world steps to strengthen Morocco’s agricultural efforts to curb greenhouse gases. We base our analysis on a comparison of national communications, updated Nationally Determined Contributions (NDCs), and findings from peer-reviewed research. We identified four main areas where Morocco can boost its impact: advanced livestock methane reduction, systematic soil carbon monitoring, precision nitrogen management, and integrated renewable energy systems. To inform these levers, we studied best practices from other six high-performing countries in the 2024 CCPI—Denmark, Sweden, India, Estonia, the Netherlands, and the Philippines—and considered how their strategies could be adapted to Morocco’s semi-arid, smallholder-dominated farming context. This study delivers four concrete, multi-phase implementation roadmaps spanning 2025–2035. These roadmaps outline the technical steps, regulatory changes, and financial mechanisms. They also specified emissions reduction targets associated with each pillar: 15–30% for livestock methane, 0.3–0.8 tons of carbon per hectare per year for soil carbon sequestration, 18% for precision nitrogen management, and fossil fuel displacement through five renewable energy initiatives. The roadmaps are designed to inform the next update of Morocco’s Generation Green strategy and support the country’s 2030 NDC goal of a 45.5% emission reduction. Full article

Gaseous emissions from livestock facilities pose environmental and health concerns. Monitoring pollutant gases is essential to mitigate impact and enhance the sustainability of livestock systems. Emerging Artificial Intelligence (AI) technologies—particularly Artificial Neural Networks (ANNs)—offer advanced tools to address these challenges by improving livestock monitoring and management. Following PRISMA guidelines, 18 studies published between 2007 and 2024 were selected from Web of Science^® and Scopus^®. Most research was conducted in Europe (55%), primarily focusing on cattle and swine. Among gases, ammonia (NH₃) was predicted in 50% of studies and methane (CH₄) in 35%. The most common ANN architecture was the Multilayer Perceptron (MLP), trained mainly with backpropagation algorithms and validated using the Root Mean Square Error (RMSE). The results show that ANN models consistently outperformed traditional statistical approaches, offering greater prediction accuracy. Future research should focus on identifying optimal ANN structures for precise emission prediction, accounting for environmental variability, reducing dataset bias, and combining ANN with statistical models to develop hybrid approaches that further improve livestock management and sustainability. Full article

Accurate detection of cattle and sheep is a core task in precision livestock farming. However, the complexity of agricultural settings, where visible light images perform poorly under low-light or occluded conditions and infrared images are limited in resolution, poses significant challenges for current smart monitoring systems. To tackle these challenges, this study aims to develop a robust multimodal fusion detection network for the accurate and reliable detection of cattle and sheep in complex scenes. To achieve this, we propose YOLO-MSLT, a multimodal fusion detection network based on YOLOv10, which leverages the complementary nature of visible light and infrared data. The core of YOLO-MSLT incorporates a Cross Flatten Fusion Transformer (CFFT), composed of the Linear Cross-modal Spatial Transformer (LCST) and Deep-wise Enhancement (DWE), designed to enhance modality collaboration by performing complementary fusion at the feature level. Furthermore, a Content-Guided Attention Feature Pyramid Network (CGA-FPN) is integrated into the neck to improve the representation of multi-scale object features. Validation was conducted on a cattle and sheep dataset built from 5056 pairs of multimodal images (visible light and infrared) collected in the Manas River Basin, Xinjiang. Results demonstrate that YOLO-MSLT performs robustly in complex terrain, low-light, and occlusion scenarios, achieving an mAP@0.5 of 91.8% and a precision of 93.2%, significantly outperforming mainstream detection models. This research provides an impactful and practical solution for cattle and sheep detection in challenging agricultural environments. Full article

The demand for food is increasing with the rise in the human population. Among foods, meat is an essential part of human nutrition. Meat provides good-quality protein and all the micronutrients needed by humans. In addition, it also contains some bioactive compounds that are good for human health. Increasing demand, together with concerns over food safety, requires new approaches to guarantee a sustainable, safe, and healthy meat supply chain. The only way to get over these challenges is through technological innovations that are capable of enhancing the safety, quality, and sustainability of meat. Herein, this review identifies the need for new methods of rapid microbial detection, biosensors, AI-based monitoring, innovative processing and preservation techniques, precision livestock farming, resource-efficient feed and water management, alternative protein sources, and circular economy approaches. In particular, this review examines some meat analogs like cultured meat, hybrid products, and microbial proteins as environmentally friendly and nutritionally balanced alternatives. These changes in technology can also bring benefits to consumers in terms of their health. The health benefits of these technological innovations for consumers go beyond just safety, including improved nutritional profiles, functional bioactive ingredients, and the prevention of antimicrobial resistance. The review further analyzes policies, regulatory frameworks, and ethical considerations necessary to achieve consumer trust and social acceptance, including the global alignment of standards, certification, labeling, and all issues related to ethics. Furthermore, AI, IoT, Big Data, and nutritional technologies represent new emerging trends able to unleash new opportunities for the optimization of production, quality control, and personalized nutrition. Full article

Extensive goat farming is the dominant livestock system in the Mediterranean region, where woody rangelands represent essential forage resources for goats. Understanding how goats move and select vegetation within these heterogeneous landscapes–and how these patterns are shaped by herding decisions-is critical for improving grazing management. This study investigated the spatio-temporal movement behavior of a goat flock in a complex woody rangeland using GPS tracking combined with GIS-based vegetation and land morphology mapping. The influence of seasonal changes in forage availability and the shepherd’s management on movement trajectories and vegetation selection was specifically examined over two consecutive years. Goat movement paths, activity ranges, and speed differed among seasons and years, reflecting changes in resource distribution, physiological stage, and herding decisions. Dense oak woodland and moderate shrubland were consistently the most selected vegetation types, confirming goats’ preference for woody species. The shepherd’s management—particularly decisions on grazing duration, route planning, and provision or withdrawal of supplementary feed—strongly affected movement characteristics and habitat use. Flexibility in adjusting grazing strategies under shifting economic conditions played a crucial role in shaping spatial behavior. The combined use of GPS devices, GIS software, vegetation maps, and direct observation proved to be an effective approach for assessing movement behavior, forage selection and grazing pressure. Such integration of technological and classical methods provides valuable insights into diet composition and resource use and offers strong potential for future applications in precision livestock management. Real-time monitoring and decision support tools based on this approach could help farmers optimize grazing strategies, improve forage utilization, and support sustainable rangeland management. Full article

Accurate monitoring of cattle behavioral time budgets is crucial for early disease detection and welfare assessment. Changes in durations of standing, lying, and eating are known to be early indicators of health issues such as lameness and metabolic disorders. To enable low-cost, non-invasive, and real-time monitoring, this study proposes a lightweight cattle behavior recognition method based on an improved YOLO11n architecture. The model enhances multi-scale feature integration through a generalized efficient layer aggregation network (GELAN), improves feature extraction via a multidimensional collaborative attention (MCA) mechanism, and achieves efficient cross-scale fusion using a bidirectional feature pyramid network (BiFPN). Depthwise separable convolution (DWConv) is incorporated to reduce computational load. Experimental results demonstrate high recognition accuracy, with mAP@0.5 values of 91.2%, 91.0%, and 93.9% for standing, lying, and eating, respectively. The model was subsequently compressed using a Layer-adaptive Magnitude-based Pruning (LAMP) algorithm, resulting in a final model of only 1.06 × 10⁶ parameters, a computational cost of 6.3 GFLOPS, and a weight size of 2.4 MB, while retaining 90.7% mAP@0.5. This highly efficient system is suitable for deployment on resource-constrained edge devices, providing a practical tool for continuous cattle monitoring. It offers a viable pathway for farmers to adopt precision livestock farming practices, facilitating early health intervention and promoting animal welfare. Full article