Search Results (130)

Accurate dairy cow behavior recognition is important for health monitoring, welfare assessment, and early warning in smart livestock farming. However, recognizing fine-grained behaviors such as feeding, drinking, and rumination remains difficult in real barns because of occlusion, complex backgrounds, subtle motion changes, and class imbalance. This study proposes a behavior recognition method that integrates temporal modeling and behavioral priors. The Contrastive Language–Image Pre-training (CLIP) visual encoder is used as the feature extraction backbone, while two temporal adapters are introduced to model dynamic information across consecutive video frames. Dairy cow behavior recognition is further decoupled into posture recognition and action recognition, and a behavioral prior loss is designed to softly constrain unlikely posture–action combinations, such as lying with feeding or lying with drinking. On the test set, the proposed method achieves a five-class accuracy of 75.45%, a five-class Macro-F1 of 0.7246, and an Action Macro-F1 of 0.7605, outperforming the CLIP baseline and several representative video recognition models. These results indicate that the proposed method can support non-contact monitoring of key dairy cow behaviors for practical barn management. Full article

The global cattle industry is experiencing significant growth, requiring advanced methods for monitoring animal health and welfare to ensure productivity and sustainability. Traditional manual monitoring techniques are labor-intensive and often impractical for large-scale operations. This review provides a comprehensive analysis of existing and emerging computer vision tools applied to the monitoring of cattle health and welfare. By systematically examining studies across major databases, this paper addresses six key research questions focusing on (1) the issues addressed by computer vision technologies, (2) data acquisition systems, (3) implemented techniques and algorithms, (4) performance outcomes, (5) challenges faced, and (6) potential applications for underexplored health and welfare aspects in cattle farming. The findings show that computer vision technologies have significantly progressed in areas such as body condition score detection, lameness detection, weight estimation, estrus detection, monitoring of feeding and drinking behavior, breathing detection, and recognition of general behaviors. Despite the progress, challenges such as variability in environmental conditions, the need for large annotated datasets, and the high cost of advanced imaging equipment persist. The review emphasizes future research opportunities to address these challenges by focusing on disease-specific monitoring. This review aims to provide veterinarians, farmers, and animal health professionals with greater insight into computer vision technologies and to promote their adoption by discussing their practical applications. Full article

Mastitis is a major and costly dairy disease that reduces milk yield and quality and harms animal welfare. This study evaluated infrared thermography (IRT) combined with machine learning (ML) for non-invasive mastitis screening in dairy cows and explored links with biological and feeding-system variables in Egyptian farms. A total of 976 thermal udder images obtained from 488 Holstein cows were used, including 708 healthy and 268 mastitic images. Images were captured before milking, processed with CLAHE, resized to 224 × 224 pixels, and split using cow-level grouping before augmentation to prevent animal-level data leakage. The training set contained 780 original images and was augmented to a balanced 4708-image set (2354 per class), while the held-out test set remained unaugmented, with 196 original images (142 healthy and 54 mastitic). EfficientNetB3 with global average and max pooling extracted 3072 thermal features, and ten ML classifiers were evaluated. In the image-level hold-out evaluation, MLP achieved the best performance (accuracy = 86.22%, AUC = 0.9184, sensitivity = 74.07%, specificity = 90.85%), followed by SVM (accuracy = 83.67%, AUC = 0.8963). A separate group-based five-fold cross-validation yielded a more conservative AUC of 0.6812 ± 0.1323 and accuracy of 0.6244 ± 0.0642. Logistic regression analyses did not identify statistically significant associations between model predictions and somatic cell count (SCC), California Mastitis Test (CMT), blood biomarkers, or nutritional variables at p < 0.05. Ration A (Delta Misr) showed a higher observed mastitis incidence (20/40; 50.0%) than Ration B (Copenhagen; 16/45; 35.6%), but nutritional predictors were not statistically significant, indicating that farm-level confounding should be considered. Overall, IRT with ML remains a promising non-invasive screening approach, but broader multicenter datasets and independent external validation are needed before routine farm deployment. Full article

Accelerometer sensors and artificial intelligence (AI) are reshaping automated behavior monitoring in precision livestock management, yet their joint deployment on extensive rangelands is constrained by energy and bandwidth budgets. Low-Power Long-Range Wide-Area Network (LoRaWAN) collars address these constraints by compressing the raw tri-axial signal on the device into a single scalar per reporting interval, the Motion Index (MI). This onboard compression preserves enough signal to separate active behaviors but discards the per-axis and frequency content that fine-grained classification typically relies on. On a dataset of 9222 labeled observations from 24 cows across four breeds, MI distinguishes walking from grazing reliably but fails to separate ruminating from resting; both correspond to a stationary animal and yield near-zero, statistically indistinguishable distributions. Earlier MI-only models reached only about 65% four-class accuracy, and ruminating was commonly merged into resting. We show that much of this loss can be recovered by treating the MI stream as a time series. Session-aware lag features, rolling statistics, and an autoregressive previous-behavior feature lift four-class macro-F1 from 0.647 to 0.94, with per-class F1 of 0.95 for ruminating and 0.92 for resting (and at least 0.92 for every behavior). In autonomous deployment the previous behavior must be predicted rather than observed; for this setting we add a Viterbi sequence-decoding step that combines the classifier’s per-step outputs with a learned behavior-transition model, recovering a substantial part of the ruminating signal from the activity stream alone while keeping walking and grazing reliable. The gain is consistent across seven classifiers and four genetically distinct breeds, indicating that it is driven by the features rather than by a specific model. Full article

Accurate positioning is essential for inspection robots in caged chicken houses, where long straight corridors, sparse textures, and repetitive structures challenge conventional methods. This paper proposes CVIWM (Coupled Visual-Inertial-Wheel Odometry with Markers), a tightly coupled state estimation method that fuses visual, inertial measurement unit (IMU), wheel odometry (WO), and fiducial marker observations within a factor graph optimization framework. Wheel odometry preintegration suppresses IMU horizontal drift and provides absolute scale, while sparse AprilTag markers (10 m spacing) periodically reset accumulated errors. Experiments in an 80 m corridor of a commercial caged chicken house at 0.116 m/s and 0.232 m/s showed that CVIWM achieves average positioning errors of 2.402 cm and 3.253 cm. This high precision ensured reliable image acquisition (image shift $<83$ pixels), enabling 95.7% dead hen detection and 98.9% egg detection accuracy. CVIWM offers a low-cost, easy-to-deploy, high-accuracy solution for automated poultry house inspection, supporting smart livestock farming. Full article

Background: Agroecology is increasingly discussed as a strategic response to the combined challenges of drought, ecological degradation, and rural vulnerability. In Morocco, this debate has become particularly urgent because agriculture now operates under persistent hydro-climatic stress, declining water availability, and strong territorial disparities between rainfed, irrigated, mountain, and oasis systems. Methods: This article is based on a structured critical review combined with an interpretive bibliometric synthesis of Moroccan and North African literature on agroecology, water stress, agricultural transition, and food-system resilience. The review was organized through conceptual framing, targeted source selection, thematic screening, and integrative synthesis. Results: Morocco is not an agroecological blank slate. Practices compatible with agroecological transition already exist across the country, including crop diversification, legume rotations, crop–livestock integration, biological regulation, organic amendments, and multifunctional production systems. However, previous reviews have mainly documented practices, projects, or sustainability initiatives without fully explaining why these remain weakly connected, poorly scaled, and insufficiently institutionalized under Moroccan conditions. This review shows that the principal barrier is not the absence of relevant practices but the absence of a coherent transition architecture capable of aligning water governance, farm economics, advisory systems, public incentives, territorial differentiation, and market valorization. The Moroccan case reveals a central paradox: agroecology is most necessary precisely where the structural conditions for its adoption are most fragile. To capture this contradiction, the paper proposes the concept of a Hydro-Agroecological Transition Trap, defined as a condition in which worsening water stress simultaneously intensifies the need for agroecological redesign and reduces the ability of farms and institutions to implement it. Conclusions: The manuscript concludes by proposing a six-pillar transition framework for Morocco based on water-smart agroecology, territorially differentiated pathways, participatory innovation, transition finance and risk-sharing, market construction, and multidimensional assessment. The originality of the study lies in shifting the analysis from a shortage of practices to a shortage of transition architecture, thereby contributing to international debates on agroecological scaling under chronic hydro-climatic stress. Full article

Point cloud semantic segmentation is a pivotal technology for realizing non-contact body measurement and refined management of livestock. However, processing sheep point clouds in smart livestock scenarios presents specific challenges, primarily due to non-rigid posture deformations and severe background interference. To address these issues, this paper proposes a novel symmetric encoder–decoder architecture named Local–Global Aggregation Network (LGA-Net), which achieves high-precision parsing of sheep point clouds by constructing a dual-scale feature aggregation mechanism. First, a Dual Attention Aggregation (DAA) module is designed to jointly encode geometric and color features, significantly enhancing the network’s ability to capture fine-grained local boundaries, such as sheep ears and hooves. Second, a Global Semantic Relation (GSR) module is introduced, utilizing spatial occupancy ratios to establish long-range dependencies, thereby effectively resolving semantic ambiguity caused by posture variations. Furthermore, a plug-and-play Dual-domain Feature Enhancement (DFE) module is proposed. By fusing bilinear interactions between explicit 3D space and implicit feature space, the DFE module constructs a high-pass filtering mechanism to suppress low-frequency background noise. Extensive experiments on a self-constructed point cloud dataset involving two semantic classes (Sheep and Fence) demonstrate that LGA-Net achieves a mIoU of 97.3%, an OA of 99.0%, and a mAcc of 97.8%. These results indicate that the proposed method outperforms existing mainstream algorithms in both segmentation accuracy and robustness. This study not only proposes a feasible solution for precise sheep extraction under the tested experimental conditions, but also provides solid technical support for subsequent automated body measurement and behavior analysis. Full article

Cattle behavior constitutes important phenotypic information reflecting animals’ health status, activity level, and welfare condition, and is therefore of considerable significance for automated monitoring and precision management in smart livestock farming. However, under complex barn conditions, cattle behavior recognition is easily affected by factors such as illumination variation, partial occlusion, background interference, and individual differences, thereby reducing recognition stability and generalization capability. To address these challenges, this study proposes a pose-driven method for cattle behavior recognition in complex barn environments. First, a 16-keypoint annotation scheme suitable for describing bovine posture, termed cow16, was constructed. Based on this scheme, OpenPose was employed to extract heatmaps (HMs) and part affinity fields (PAFs), which were then used to build an intermediate HM/PAF posture representation. Subsequently, this representation was taken as the input to a lightweight convolutional neural network for classifying three behavioral categories: stand, walk, and lying. On this basis, class-imbalance correction during training and a multi-random-seed logits ensemble strategy during inference were further introduced. In addition, knowledge distillation was adopted to transfer knowledge from a high-performance teacher model to a lightweight student model. Experimental results demonstrate that training-stage class-imbalance correction and inference-stage multi-random-seed logits ensembling exhibit strong complementarity; when combined, the AB configuration improves the test-set Macro-F1 by 3.83 percentage points. Moreover, the distilled student model still achieves competitive recognition performance while maintaining 1× inference cost, indicating a favorable trade-off between accuracy and efficiency. This study provides a useful reference for deployment-oriented cattle behavior recognition in smart farming scenarios and offers a lightweight technical basis for subsequent practical applications. Full article

Smart feedlots are increasingly adopting Precision Livestock Farming technologies to enable continuous, individual-animal monitoring and more proactive management in intensive beef production systems. This narrative review synthesises evidence from approximately 350 academic publications, of which 117 are formally cited, complemented by industry deployments and the authors’ experience in smart feedlot system development. We cover enabling digital infrastructure (power, sensing networks, wireless connectivity, and gateways), animal identification and sensing (RFID, automated weighing, wearables, and pen-side sensors), machine vision (RGB, thermal, and multispectral imaging from fixed and mobile platforms), and AI-based analytics and decision support for health, welfare, performance, and environmental management. Across the literature, key components have progressed beyond proof-of-concept toward operation under commercial constraints. Reported outcomes include reduced reliance on routine pen-rider observation and yard handling, earlier triage of emerging morbidity risk and behavioural change, and more standardised welfare auditing. Vision-based methods are repeatedly validated against trained human scorers in both on-farm and abattoir contexts, while automated weighing and image-based liveweight estimation support higher-frequency growth monitoring with low single-digit percentage error in representative studies. Precision feeding and targeted supplementation are associated with improved feed utilisation and reduced resource wastage, although effectiveness and adoption vary across animal classes and production stages. We identify priorities for robust, scalable deployment: resilient communications in harsh environments, appropriate edge–cloud partitioning under intermittent connectivity, and interoperable multi-sensor data fusion to deliver trustworthy alerts and actionable insights. Persistent barriers remain cost, durability, maintenance burden, integration and interoperability, data governance, and workforce capability. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Pig behavior recognition plays a vital role for early disease detection, animal welfare evaluation, and precision agriculture. Current deep learning methods tend to be complex, parameter intensive, or lack generalization in unstructured farming scenarios, hindering their deployment on resource-limited devices. To address this issue, we propose three optimizations based on the lightweight YOLO11n: (1) embed SCSA-CBAM in C3k2 layers to enhance multi-scale feature discrimination; (2) introduce WFU in the neck for dynamic cross-scale feature integration; and (3) replace standard convolutions in the backbone with WTConv to reduce the computational overhead. Initialized with COCO pre-trained weights, the proposed model employs a two-stage transfer learning approach combined with data augmentation. On a self-built six-category pig behavior dataset based on public datasets of 2480 original images (split into training/validation sets at an 8:2 ratio via stratified random sampling), the optimized YOLO11n-SCSA-WFU-WT achieves an mAP@0.5 of 0.974 and mAP@0.5:0.95 of 0.785, with 3.40 M parameters, 7.8 GFLOPs, and 72.28 FPS, while achieving substantial accuracy improvements over the baseline and maintaining lightweight performance over the baseline. Ablation experiments verify the independent contributions of each module, and comparisons with mainstream models demonstrate a more favorable accuracy–efficiency trade-off. The overall results confirm the effectiveness of our method, which facilitates real-time pig behavior detection in future smart livestock management. Full article

Accurate estrus detection is essential for dairy farm productivity, yet traditional manual and wearable methods remain limited by high labor costs, delayed responses, and animal stress. To address these challenges, we propose CE-YOLO, a lightweight YOLOv11n-based vision model tailored for edge deployment, which detects mounting behavior by integrating a Channel-Aware Downsampling (CA-Down) module to preserve small-scale features, a SimSPPF module for efficient contextual fusion, and a DySample module for dynamic spatial alignment. Experiments on a curated estrus behavior dataset demonstrate that CE-YOLO achieves a precision of 94.9% and an mAP50 of 98.2%, significantly outperforming the baseline by 3.9% and 4.6% respectively. These results validate the model as an efficient, non-intrusive solution for real-time estrus monitoring, strongly supporting the advancement of smart livestock management. Full article

Goats are important livestock species in most rural households and were amongst the first species to be domesticated. Despite this, their production is based on extensive systems, exposing them to numerous challenges affecting their productivity. This review examines the applications of precision livestock farming (PLF) and AI-driven technologies in goat management, focusing on their impacts on productivity, welfare, genetic potential, health monitoring, feeding efficiency and sustainability outcomes and identifying challenges for their adoption in smallholder and extensive systems. Unlike previous reviews that focus mainly on cattle raised under intensive systems, this review synthesizes their use in goat production and highlights technological, socio-economic and infrastructural constraints. A conventional literature review approach is used, with studies retrieved from major databases using relevant keywords. The selected studies are evaluated to assess technological applications, benefits and adoption challenges, followed by a SWOT analysis. Engineering aspects of precision livestock farming—including sensors, data connectivity, system integration, automation and scalability—are also discussed. Ideally, these technologies operate as integrated decision-support systems that jointly improve productivity, animal welfare and sustainability, rather than performing isolated tasks. However, many PLF solutions remain at low technology-readiness levels and are constrained by infrastructure gaps, sensor reliability and compatibility issues, which collectively limit adoption in smallholder systems. Future research should focus on the development of cost-effective, reliable PLF systems for smallholder producers, while policy and capacity-building initiatives are needed to enhance infrastructure, training and technology adoption for scalable implementation. Full article

Deep learning-based sheep face recognition technology significantly enhances the automation of individual sheep identification, providing critical technical support for smart livestock farming and precision agriculture. However, in real farming environments, factors such as complex backgrounds, illumination variations, and the high visual similarity of sheep faces severely constrain the comprehensive performance of recognition systems regarding accuracy and real-time capability. To address these challenges, we propose a cascaded framework comprising the WRT-DETR model for detection and LG-MobileViT for identification. WRT-DETR integrates multi-scale wavelet residual modeling and adaptive feature interaction into the RT-DETR architecture to effectively handle complex backgrounds. Subsequently, LG-MobileViT utilizes local–global collaborative modeling to distinguish fine-grained features while maintaining a lightweight footprint suitable for edge devices. Experiments conducted on a dataset of 400 individuals and 20,000 images demonstrate that WRT-DETR achieves 92.5% mAP50 in detection tasks. Furthermore, LG-MobileViT attains 98.97% recognition accuracy with a parameter size of only 4.57 MB. On edge computing platforms, the integrated system reaches an inference speed approaching 100 FPS. These results confirm that the proposed framework offers an efficient, reliable technical solution for non-contact, precise sheep identification in practical precision agriculture scenarios. Full article

Timely and accurate detection of sow oestrus is crucial for enhancing reproductive efficiency and reducing non-productive days (NPDs) in large-scale pig farms. However, traditional manual observation is labour-intensive and subjective, while cloud-based deep learning solutions face challenges such as high latency and privacy risks when applied in intensive housing environments. This study developed an edge-intelligent monitoring system that integrates deep temporal modelling with sound source localisation technology. A three-stage hierarchical screening strategy was utilised to select and deploy a lightweight Stacked-LSTM model on the resource-constrained ESP32-S3 hardware platform. This model was trained and calibrated using a high-quality acoustic dataset validated against serum reproductive hormones, specifically follicle-stimulating hormone (FSH), luteinising hormone (LH), and progesterone ($P_4$). Experimental results demonstrate that the optimised model achieved a classification accuracy of 96.17%, with an inference latency of only 41 ms, thereby fully satisfying the stringent real-time monitoring requirements while maintaining a minimal memory footprint. Furthermore, the system integrates a localisation algorithm based on Generalised Cross-Correlation with Phase Transform (GCC-PHAT). Through spatial geometric modelling, the system successfully implements the functional mapping of vocalisation events to individual gestation stalls (Stall IDs). Laboratory pressure tests validated the robustness and low-cost deployment advantages of the “edge recognition–cloud synchronization” architecture, providing a reliable technical framework for the precision management of smart livestock farming. Full article