Search Results (116)

Rural regions face significant barriers to adopting IoT technologies, due to limited connectivity, energy constraints, and poor technical infrastructure. While urban environments benefit from advanced digital systems and cloud services, rural areas often lack the necessary conditions to deploy and evaluate secure and autonomous IoT solutions. To help overcome this gap, this paper presents the Smart Rural IoT Lab, a modular and reproducible testbed designed to replicate the deployment conditions in rural areas using open-source tools and affordable hardware. The laboratory integrates long-range and short-range communication technologies in six experimental scenarios, implementing protocols such as MQTT, HTTP, UDP, and CoAP. These scenarios simulate realistic rural use cases, including environmental monitoring, livestock tracking, infrastructure access control, and heritage site protection. Local data processing is achieved through containerized services like Node-RED, InfluxDB, MongoDB, and Grafana, ensuring complete autonomy, without dependence on cloud services. A key contribution of the laboratory is the generation of structured datasets from real network traffic captured with Tcpdump and preprocessed using Zeek. Unlike simulated datasets, the collected data reflect communication patterns generated from real devices. Although the current dataset only includes benign traffic, the platform is prepared for future incorporation of adversarial scenarios (spoofing, DoS) to support AI-based cybersecurity research. While experiments were conducted in an indoor controlled environment, the testbed architecture is portable and suitable for future outdoor deployment. The Smart Rural IoT Lab addresses a critical gap in current research infrastructure, providing a realistic and flexible foundation for developing secure, cloud-independent IoT solutions, contributing to the digital transformation of rural regions. Full article

Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering and temporal concentration patterns of livestock behaviours are critical yet underexplored factors that significantly influence grassland ecosystems. This study investigated the spatiotemporal patterns of livestock behaviours under different grazing management systems and grazing-intensity gradients (GIGs) in Wenchang, China, using high-resolution GPS tracking data and machine learning classification. the K-Nearest Neighbours (KNN) model combined with SMOTE-ENN resampling achieved the highest accuracy, with F1-scores of 0.960 and 0.956 for continuous and rotational grazing datasets. The results showed that the continuous grazing system failed to mitigate grazing pressure when grazing intensity was reduced, as the spatial clustering of livestock behaviours did not decrease accordingly, and the frequency of temporal peaks in grazing behaviour even showed an increasing trend. Conversely, the rotational grazing system responded more effectively, as reduced GIGs led to more evenly distributed temporal activity patterns and lower spatial clustering. These findings highlight the importance of incorporating livestock behavioural patterns into grassland monitoring and offer data-driven insights for sustainable grazing management. Full article

This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. Although object detection models, including You Only Look Once (YOLO), EfficientDet, and sequence models, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Long Short-Term Memory (convLSTM), have improved detection and classification, significant challenges remain, including occlusions, annotation bottlenecks, dataset diversity, and limited generalizability. Existing interaction inference methods rely heavily on distance-based approximations (i.e., assuming that proximity implies social interaction), lacking the semantic depth essential for comprehensive SNA. To address this, we propose innovative methodological intersections such as pose-aware SNA frameworks and multi-camera fusion techniques. Moreover, we explicitly discuss ethical challenges and data governance issues, emphasizing data transparency and animal welfare concerns within precision livestock contexts. We clarify how these methodological innovations directly impact practical farming by enhancing monitoring precision, herd management, and welfare outcomes. Ultimately, this synthesis advocates for strategic, empathetic, and ethically responsible precision dairy farming practices, significantly advancing both dairy cow welfare and operational effectiveness. Full article

Pastoral activities are an essential part of the cultural and ecological landscape of Central Italy. This traditional practice supports local economies, maintains biodiversity, and contributes to the sustainable use of natural resources. Understanding livestock behavior in response to environmental variability is essential for improving grazing management and animal welfare and ensuring the sustainability of these systems. This study evaluated the movement patterns of sheep grazing on pastures with differing vegetation indices in the Sibillini Mountains. Twenty lactating ewes foraging on two different pastures were monitored from June to October 2023 using GPS collars and accelerometers. GPS tracks were segmented using the Expectation Maximization Binary Clustering (EmBC) method to characterize movement behaviors, such as foraging, traveling, and resting. The NDVI was used to characterize vegetation dynamics, showing notable differences between the two pastures and across the grazing season. Additive mixed models were used to analyze data, accounting for individual variability and temporal autocorrelation in the sample. The results suggest that variations in the NDVI influence grazing behavior, with sheep in areas of lower vegetation density exhibiting increased movement during foraging. These findings provide valuable insights for optimizing grazing practices and promoting sustainable land use. Full article

Vertebrate molecular genetic research methods typically employ single genetic loci (monolocus markers) and those involving a variable number of loci (multilocus markers). The former often employ microsatellites that ensure accuracy in establishing inbreeding, tracking pan-generational dynamics of genetic parameters, assessing genetic purity, and facilitating genotype/phenotype correlations. They also enable the determination and identification of unique alleles by studying and managing marker-assisted breeding regimes to control the artificial selection of agriculturally important traits. Microsatellites consist of 2–6 nucleotides that repeat numerous times and are widely distributed throughout genomes. Their main advantages lie in their ease of use for PCR amplification, their known genome localization, and their incredible polymorphism (variability) levels. Robust lab-based molecular technologies are supplemented by high-quality statistics and bioinformatics and have been widely employed, especially in those instances when more costly, high throughput techniques are not available. Here, we consider that human and livestock microsatellite studies have been a “roadmap” for the genetics, breeding, and conservation of wildlife and rare animal breeds. In this context, we examine humans and other primates, cattle and other artiodactyls, chickens and other birds, carnivores (cats and dogs), elephants, reptiles, amphibians, and fish. Studies originally designed for mass animal production have thus been adapted to save less abundant species, highlighting the need for molecular scientists to consider where research may be applied in different disciplines. Full article

The mounting global population and the challenges posed by climate change underline the need for sustainable food production systems. This review synthesizes evidence for a dual-track bioeconomy, green (terrestrial plants and insects) and blue (aquatic algae), as complementary pathways toward sustainable nutrition. A comprehensive review of the extant literature, technical reports, and policy documents published between 2015 and 2025 was conducted, with a particular focus on environmental, nutritional, and techno-economic metrics. In addition, precision agriculture datasets, gene-editing breakthroughs, and circular biorefinery case studies were extracted and compared. As demonstrated in this study, the use of green resources, such as legumes, oilseeds, and edible insects, results in a significant reduction in greenhouse gas emissions, land use, and water footprints compared with conventional livestock production. In addition, these alternative protein sources offer substantial benefits in terms of bioactive lipids. Blue resources, centered on micro- and macroalgae, furnish additional proteins, long-chain polyunsaturated fatty acids, and antioxidant pigments and sequester carbon on non-arable or wastewater substrates. The transition to bio-based resources is facilitated by technological innovations, such as gene editing and advanced extraction methods, which promote the efficient valorization of agricultural residues. In conclusion, the study strongly suggests that policy support be expedited and that research into bioeconomy technologies be increased to ensure the sustainable meeting of future food demands. Full article

Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. Likewise, computer vision and deep learning models are driven by CNNs to act intelligently in improving productivity and animal management for sustainable animal husbandry. In animal husbandry, CNNs play a vital role in the management and monitoring of livestock’s health and productivity due to their high-performance accuracy in analyzing images and videos. Monitoring animals’ health is important for their welfare, food abundance, safety, and economic productivity. This paper aims to comprehensively review recent advancements and applications of relevant models that are based on CNNs for livestock health monitoring, covering the detection of their various diseases and classification of their behavior, for overall management gain. We selected relevant articles with various experimental results addressing animal detection, localization, tracking, and behavioral monitoring, validating the high-performance accuracy and efficiency of CNNs. Prominent anchor-based object detection models such as R-CNN (series), YOLO (series) and SSD (series), and anchor-free object detection models such as key-point based and anchor-point based are often used, demonstrating great versatility and robustness across various tasks. From the analysis, it is evident that more significant research contributions to animal husbandry have been made by CNNs. Limited labeled data, variation in data, low-quality or noisy images, complex backgrounds, computational demand, species-specific models, high implementation cost, scalability, modeling complex behaviors, and compatibility with current farm management systems are good examples of several notable challenges when applying CNNs in animal husbandry. By continued research efforts, these challenges can be addressed for the actualization of sustainable animal husbandry. Full article

Effective health management during the transition period depends on early disease detection, which can be achieved through continuous monitoring using precision livestock farming tools. This study assessed reticulorumen temperature, rumination time, and activity in dairy cows during the periparturient period under summer heat stress. We hypothesized differences in these parameters between healthy (HE) cows and those developing postpartum disorders (DI). Forty clinically healthy, multiparous cows were monitored from 5 days prepartum to 14 days after calving (days in milk; DIM). A cow was considered healthy and allocated to the HE group (n = 26) if she was not affected by any postpartum health disorders until the end of the study period. A cow was considered diseased and allocated to the DI group (n = 14) if she had been diagnosed with mastitis, metritis, lameness, or ketosis. Weather loggers recorded barn microclimate data, while rumination, activity, and rumen temperature were tracked using a microphone-based sensor in the neck collar (Ruminact HR) and rumen bolus (Smaxtec). THI values remained above 68 throughout the study, peaking at 80, indicating sustained heat stress. Rumen temperature ranged between 39 and 41 °C and moderately correlated with THI (correlation coefficient was 0.27; 95% CI: 0.20; 0.33; p < 0.0001). Both groups exhibited a nadir in rumen temperature at calving, with no differences. Rumination time declined prepartum, reaching its lowest at 2 DIM in DI cows. It was significantly affected by days around calving, postpartum disorders, and THI. Activity increased prepartum and normalized by 4 DIM in HE cows, while DI cows showed higher activity at 4 DIM, stabilizing by 5–7 DIM. These findings underscore the value of precision monitoring tools for early disease detection and intervention. Full article

In a 1975 review, red kangaroos in the arid rangelands of Australia were said to be favoured with an anomalous prosperity following the introduction of ruminant livestock. In the western and central locations reviewed, this was not sustained, but in the sheep rangelands of Southern Australia, it is often claimed that such prosperity continues. Here, as elsewhere, the marsupial herbivore guild (kangaroos, wallabies, bettongs and bandicoots) has been simplified by the extinction of the smaller species (the anomaly), while large kangaroos remain abundant. However, the mammalian herbivore guild has gained complexity with not only the introduction of managed ruminant livestock, some of which run wild, but also game like rabbits. We studied the population dynamics, habitat selection and individual mobility of red, western and eastern grey kangaroos, common wallaroos, Merino sheep, feral goats and European rabbits at Fowlers Gap Station in far northwestern New South Wales, Australia. This site is representative of the arid chenopod (Family: Chenopodiaceae) shrublands stocked with sheep, where sheep and red kangaroos dominate the mammalian herbivores by biomass. The study site comprised two contiguous pairs of stocked and unstocked paddocks: a sloping run-off zone and a flat run-on zone, covering a total area of 2158 ha. This three-year study included initial rain-deficient (drought) months followed by more regular rainfall. Red kangaroos showed avoidance of sheep when given the opportunity and heightened mobility in response to localized drought-breaking storms and dispersion of the sheep flock at lambing. Western grey kangaroos were sedentary and did not dissociate from sheep. These effects were demonstrated at the population level and the individual level through radio-tracking a small cohort of females. The other kangaroo species and goats were transient and preferred other habitats. Rabbits were persistent and localized without strong interactions with other species. The results are discussed with a focus on the red kangaroo and some causes for its resilience in the sheep rangelands. Full article

Accurately predicting livestock movement is a cornerstone of precision agriculture, enabling smarter resource management, improved animal welfare, and enhanced productivity. However, the unpredictable and dynamic nature of livestock behavior poses significant challenges for traditional mobility prediction models. This study introduces LSTM-H, a hybrid deep learning model that combines the sequential learning power of Long Short-Term Memory (LSTM) networks with the real-time correction capabilities of Kalman Filters (KFs) to enhance livestock movement prediction within UAV-based monitoring frameworks. The results demonstrate that LSTM-H achieves a mean error of just 11.51 m for the first step and 40.68 m over a 30-step prediction horizon, outperforming state-of-the-art models by 4.3–14.8 times. Furthermore, LSTM-H exhibits robustness across noisy and dynamic conditions, with a 90% probability of errors below 13 m, as shown through cumulative error analysis. This enhanced accuracy enables UAVs to optimize flight trajectories, reducing energy consumption and improving monitoring efficiency in real-world agricultural settings. By bridging deep learning and adaptive filtering, LSTM-H not only enhances prediction accuracy but also paves the way for scalable, real-time livestock and UAV monitoring systems with transformative potential for precision agriculture. Full article

The Spiny-tailed Lizard (Uromastyx aegyptia), a vulnerable species native to the desert and semi-desert regions of the Middle East, remains poorly understood, particularly regarding its daily activity patterns in northern Saudi Arabia. This study, conducted in the Ha’il region, aimed to examine these patterns, assess the influence of soil temperature on activity, and identify potential threats to the species. The results revealed that soil temperature significantly affected the lizard’s activity patterns. During spring, Spiny-tailed Lizards were more active, spending around 25% of the day engaged in various behaviours, while their activity decreased to less than 20% in summer. In autumn and winter, the lizards did not follow a consistent daily activity, becoming active only when surface temperatures exceeded 35 °C. The absence of tracks and sightings in January suggests the species enters a state of complete brumation during this month. While no predation events were recorded via trail cameras, human disturbance from livestock and vehicles was observed in spring and summer. Although the disturbance was minor, reducing this type of human-caused disturbance should be taken into consideration when designing any protection programs. Furthermore, the long-term monitoring of this lizard’s daily and seasonal activity patterns is recommended in order to better understand its adaptability to environmental changes, especially those driven by climate fluctuations. Full article

Modern poultry production demands scalable and non-invasive methods to monitor animal welfare, particularly as broiler strains are increasingly bred for rapid growth, often at the expense of mobility and health. This study evaluates two advanced computer vision techniques—Optical Flow and the Unrest Index—to assess movement patterns in broiler chickens. Three commercial broiler strains (Hybro^®, Cobb^®, and Ross^®) were housed in controlled environments and continuously monitored using ceiling-mounted video systems. Chicken movements were detected and tracked using a YOLO model, with centroid data informing both the Unrest Index and distance walked metrics. Optical Flow velocity metrics (mean, variance, skewness, and kurtosis) were extracted using the Farnebäck algorithm. Pearson correlation analyses revealed strong associations between Optical Flow variables and traditional movement indicators, with average velocity showing the strongest correlation to walked distance and the Unrest Index. Among the evaluated strains, Cobb^® demonstrated the strongest correlation between Optical Flow variance and the Unrest Index, indicating a distinct movement profile. The equipment’s movement and the camera’s slight instability had a minimal effect on the Optical Flow measurement. Still, its strong correlation with the Unrest Index and walking distance accredits it as an effective method for high-resolution behavioral monitoring. This study supports the integration of Optical Flow and Unrest Index technologies into precision livestock systems, offering a foundation for predictive welfare management at scale. Full article

The relentless drive to meet global demand for poultry products has pushed for rapid intensification in chicken farming, dramatically boosting efficiency and yield. Yet, these gains have exposed a host of complex welfare challenges that have prompted scientific scrutiny and ethical reflection. In this review, I critically evaluate recent innovations aimed at mitigating such concerns by drawing on advances in behavioral science and digital monitoring and insights into biological adaptations. Specifically, I focus on four interconnected themes: First, I spotlight the complexity of avian sensory perception—encompassing vision, auditory capabilities, olfaction, and tactile faculties—to underscore how lighting design, housing configurations, and enrichment strategies can better align with birds’ unique sensory worlds. Second, I explore novel tools for gauging emotional states and cognition, ranging from cognitive bias tests to developing protocols for identifying pain or distress based on facial cues. Third, I examine the transformative potential of computer vision, bioacoustics, and sensor-based technologies for the continuous, automated tracking of behavior and physiological indicators in commercial flocks. Fourth, I assess how data-driven management platforms, underpinned by precision livestock farming, can deploy real-time insights to optimize welfare on a broad scale. Recognizing that climate change and evolving production environments intensify these challenges, I also investigate how breeds resilient to extreme conditions might open new avenues for welfare-centered genetic and management approaches. While the adoption of cutting-edge techniques has shown promise, significant hurdles persist regarding validation, standardization, and commercial acceptance. I conclude that truly sustainable progress hinges on an interdisciplinary convergence of ethology, neuroscience, engineering, data analytics, and evolutionary biology—an integrative path that not only refines welfare assessment but also reimagines poultry production in ethically and scientifically robust ways. Full article

Antimicrobial resistance (AMR) is a multi-sectoral, systemic, and global issue worldwide. Antimicrobial use (AMU) is a key factor in the selection of resistant bacteria within different ecological niches, from agriculture to food-producing animals to humans. There is a question regarding the extent to which the use of antibiotics in livestock production and the primary food production sector influences the selection and transmission of resistant bacteria and/or resistant genes throughout the food chain and thus contributes to the complexity in the development of AMR in humans. Although the trends in the prevalence of foodborne pathogens have changed over time, the burden of ecological niches with resistance genes, primarily in commensal microorganisms, is of concern. The implementation of the harmonized surveillance of AMU and AMR would provide comprehensive insights into the actual status of resistance and further interventions leading to its reduction. Tracking AMR in different ecological niches by applying advanced genome-based techniques and developing shared AMR data repositories would strengthen the One Health concept. Full article

Single-class object detection, which focuses on identifying, counting, and tracking a specific animal species, plays a vital role in optimizing farm operations. However, dense occlusion among individuals in group activity scenarios remains a major challenge. To address this, we propose YOLO-SDD, a dense detection network designed for single-class densely populated scenarios. First, we introduce a Wavelet-Enhanced Convolution (WEConv) to improve feature extraction under dense occlusion. Following this, we propose an occlusion perception attention mechanism (OPAM), which further enhances the model’s ability to recognize occluded targets by simultaneously leveraging low-level detailed features and high-level semantic features, helping the model better handle occlusion scenarios. Lastly, a Lightweight Shared Head (LS Head) is incorporated and specifically optimized for single-class dense detection tasks, enhancing efficiency while maintaining high detection accuracy. Experimental results on the ChickenFlow dataset, which we developed specifically for broiler detection, show that the n, s, and m variants of YOLO-SDD achieve AP_50:95 improvements of 2.18%, 2.13%, and 1.62% over YOLOv8n, YOLOv8s, and YOLOv8m, respectively. In addition, our model surpasses the detection performance of the latest real-time detector, YOLOv11. YOLO-SDD also achieves state-of-the-art performance on the publicly available GooseDetect and SheepCounter datasets, confirming its superior detection capability in crowded livestock settings. YOLO-SDD’s high efficiency enables automated livestock tracking and counting in dense conditions, providing a robust solution for precision livestock farming. Full article