Search Results (1,925)

The picking of tea leaves in tea gardens requires multiple batches in the short and valuable tea harvest period. To realize timely and efficient tea plucking, it is feasible to use unmanned aerial vehicles (UAV) for tea shoot detection in large tea gardens. For the typical small targets of tea buds in unmanned aerial vehicle (UAV) aerial images, it is necessary to design an efficient tea buds detection model. In order to improve the accuracy and the speed of the tea buds detection in the UAV images, we designed the SH-CoordMapping hash space mapping algorithm to accelerate the remerging of the detection results into the original image. The C2PSA-BI module and the CARAFE upsampling module are applied to improve detail preservation during feature fusion. A lightweight detection head is further used to reduce redundant computation in the detection stage. By comparing with the traditional detection methods, it can be proved that the SWO sections are necessary for UAV-scale tea shoots detection. Based on the accuracy and the number of model parameters, the YOLO11n model with slice size as 640 and overlap rate as 0.1 performs the best. The TSDet-UAV was deployed on the NVIDIA Jetson Orin NX chip to construct an inspection system capable of real-time acquisition and detection. The experimental results demonstrate that the proposed TSDet-UAV achieves excellent performance, recording an mAP₅₀ of 52.9% on the constructed UAV-TS dataset while maintaining high efficiency. With a parameter size of 2.4 M and a total processing time of 1.32 s per high-resolution image under TensorRT FP16, the processing speed is highly suitable for real-time edge deployment on agricultural UAV platforms. The UAV image-based tea garden shoot inspection platform proposed in this paper can effectively confirm the growth status of tea shoots, assisting farm management in formulating precise picking plans. Full article

Large-scale wind integration introduces significant harmonic degradation and resonance risks. Traditional strategies primarily targeting Total Harmonic Distortion (THD) often struggle with individual node violations and high investment costs. To address these challenges, this paper proposes a two-stage sequential coordination strategy for Passive Power Filters (PPFs) and Active Power Filters (APFs). First, stochastic harmonic emission and frequency-domain power flow models are developed to characterize wind-induced harmonic propagation. Second, a sequential optimization framework is established to minimize Life Cycle Cost (LCC). In the first stage, PPF siting and sizing are optimized for cost-effective, system-wide mitigation of low-order harmonics while ensuring THD compliance. The second stage utilizes targeted APF deployment to precisely suppress residual high-order violations and localized resonance. Chance-constrained programming is incorporated to manage wind power uncertainty, enhancing the scheme’s robustness. Simulations on an IEEE 17-bus system demonstrate that the proposed method effectively balances harmonic suppression performance with economic efficiency, providing a robust and cost-effective solution for wind farm power quality management. Full article

Genetic resources of small ruminants are essential for food security in arid regions; however, basic data for each breed in Saudi Arabia remain incomplete. This study establishes a comprehensive national database through a systematic survey of 104 farms, covering 21,214 heads of livestock (sheep and goats) across the kingdom’s primary agro-ecological zones between January and October 2025. Although national census data indicate that major breeds of sheep such as Naeemi, Najdi, Arabi, and Harri or goats such as Ardi exceed the FAO’s numerical thresholds for “not at risk,” our analysis reveals a fundamental paradox of “genetic vulnerability,” defined as a high risk of inbreeding depression and genetic stagnation despite high census numbers. The results show significant regional variations in prolificacy (p < 0.05), with the southern region displaying a substantial productivity gap compared to the central and eastern regions, mainly due to reliance on traditional grazing (46.7%) and limited infrastructure. This vulnerability is driven by a high risk of systematic inbreeding, with 65.7% of breeders acquiring sires from their own herds, a situation worsened by a severe 80% shortage of high-quality breeding males in the central region. Furthermore, selection criteria heavily emphasize esthetic phenotypic traits (over 80%) rather than production indicators (less than 8%), hindering genetic progress. Correlation analysis showed that higher farmer education levels were negatively associated with reproductive challenges (r = −0.216), while high feed prices remained a near-universal obstacle (97.1%). To mitigate these risks, we recommend implementing region-specific sire exchange programs to break closed breeding loops and establishing a national performance recording system to shift selection focus from phenotypic traits to measurable productivity. This study provides a vital, evidence-based framework for transitioning toward data-driven, resilient conservation and breeding strategies. Full article

With the large-scale integration of offshore wind farms (OWFs), harmonic issues caused by the interaction between high-frequency switching of converters and complex network impedances pose severe challenges to power quality. Traditional harmonic monitoring heavily relies on post-event fixed-threshold alarm mechanisms, which struggle to achieve early warning during the low-distortion sub-health operation stage and lack the capability for multi-dimensional tracing of harmonic degradation sources. To address these limitations, this paper proposes a deep warning network for grid harmonics combining multi-step regression and multi-dimensional tracing within a unified multi-task learning (MTL) architecture. First, a deep shared feature encoder, integrating a bi-directional long short-term memory (Bi-LSTM) network with a multi-head self-attention (MHSA) mechanism, is utilized to extract high-order temporal coupling features between meteorological evolution and multi-node electrical states. Subsequently, the main task branch executes a k-step-ahead multivariate time-series regression to accurately predict the evolution trend of total harmonic distortion (THD) at both the point of common coupling (PCC) and the turbine terminal. Simultaneously, the auxiliary task branch performs multi-label micro-state classification based on relative degradation thresholds, achieving fine-grained multi-dimensional tracing covering spatial nodes, electrical attributes, and their joint micro-states. Experimental results on real-world OWF operational data demonstrate that through the joint optimization of regression and tracing tasks, the proposed MultiDimKStepMTL model significantly improves time-series prediction accuracy, achieving a $10.3\%$ relative improvement over single-task baselines, while substantially reducing computational overhead. This research successfully advances grid harmonic monitoring from passive response to proactive micro-state early warning, providing a solid, highly interpretable data-driven foundation for active filter control of offshore wind clusters. Full article

Due to the controlled characteristics of fault current in doubly fed wind farms and the distributed capacitance effects of transmission lines, traditional distance protection is prone to failure or maloperation during high resistance faults. To improve protection reliability, this paper proposes a novel time–domain distance protection scheme based on the hybrid-π model. First, the improved time–domain fault differential equations are formulated based on the hybrid-π model, incorporating ground capacitance and integrating electrical quantities at both ends of the line. Next, the composite weight matrix integrating transient mutation weights and fitting error weights is introduced and embedded within a nonlinear least-squares framework. This enables the algorithm to adaptively distinguish and suppress unreliable data, simultaneously achieving transient disturbance resistance and rapid steady-state convergence. Finally, a 220 kV double-fed wind power grid-connected system with a 100 km transmission line is built in MATLAB/Simulink for simulation. Different types of faults under various locations and transition resistances are simulated to verify the effectiveness of the proposed scheme. Full article

Pasteurellosis is a major bacterial disease of domestic rabbits, commonly associated with respiratory disorders, abscesses, reproductive pathology, and systemic infections. This study investigated the occurrence, clinical manifestations, pathological lesions, and antimicrobial susceptibility of bacterial isolates obtained from rabbits raised in traditional extensive systems in western Romania, with identification of Pasteurella multocida performed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). A total of 308 rabbits from 23 holdings were clinically examined, necropsied when applicable, and subjected to bacteriological analysis. Clinical signs compatible with pasteurellosis were observed in 132 rabbits (42.9%), including respiratory diseases, abscess formation, otitis, and reproductive disorders. Samples collected from affected and deceased rabbits were cultured and analyzed using MALDI-TOF MS, confirming 87 isolates as P. multocida. Antimicrobial susceptibility testing using the VITEK 2 system revealed high resistance to tetracyclines (63.22%) and beta-lactams (55.17%), while higher susceptibility was observed for enrofloxacin (91.95%), gentamicin (89.66%), ciprofloxacin (86.21%), and florfenicol (80.46%). The presence of multidrug-resistant isolates highlights the need for laboratory-guided antimicrobial therapy and improved biosecurity measures in traditional rabbit holdings. Full article

The development of offshore wind energy has increased the demand for reliable submarine transmission systems. In South Africa, research remains constrained due to the lack of operational offshore wind farms, despite favorable geographical conditions and persistent energy challenges such as load-shedding. Submarine cable faults, primarily caused by manufacturing deficiencies, environmental factors, and human activities, contribute significantly to system downtime while accounting for only a small portion of overall installation costs. This study reviews submarine cable fault identification, classification, pre-determination, and localization techniques. Conventional methods, including time-domain reflectometry, the Murray loop, the Varley loop, and impulse-based techniques, are reviewed alongside artificial neural network models, such as convolutional and deep learning architectures. Findings imply that traditional techniques offer low error margins but lack the accuracy needed for pinpointing exact faults, as faults may extend over several kilometers. In contrast, neural network-based methods, particularly when integrated with signal processing methods, significantly improve fault classification and localization accuracy. The study concludes that hybrid approaches combining conventional diagnostic techniques with neural networks offer a robust framework for submarine cable fault analysis, providing real-world solutions to enhance reliability and efficiency in future offshore wind transmission systems. Full article

With increasing water scarcity and growing food demand, enhancing agricultural productivity has become a pressing necessity, aligning with the Sustainable Development Goals (SDGs). Maize is a strategic crop, yet under surface irrigation, modern technologies are required to optimize irrigation efficiency and reduce water losses. Two field trials were conducted during the summer seasons of 2024 and 2025 on a private farm in Banha, Qalyubia Governorate, Egypt, using a three-replication split-block design. This study evaluated three land slopes (0, 0.05, and 0.15%) and two furrow lengths (50 and 75 m) under furrow irrigation in clay loam soil, using the maize hybrid “Single Cross 2036.” The results demonstrated that both furrow length and land slope significantly affected all measured parameters. Shorter furrows (50 m) consistently outperformed longer ones (75 m), achieving better growth parameters, higher grain yield, improved harvest index, and enhanced irrigation water productivity. Regarding land slope, the 0.15% slope produced the best results, although it was not significantly different from the 0.05% slope in most cases. The interaction between furrow length and land slope was significant; the combination of 50 m furrows with 0.15% slope produced the highest values across all parameters. For longer furrows (75 m), the gentler 0.05% slope was more effective than the steeper 0.15% slope. Notably, 50 m furrows, even with 0% slope, performed better than 75 m furrows with the optimal 0.05% slope, indicating that furrow length is more critical than slope for maximizing maize productivity in clay loam soils. Economic analysis confirmed these findings, with the combination of 50 m furrows and 0.15% slope achieving the highest net return (29,565 EGP ha⁻¹) and revenue-to-cost ratio (1.38), representing a substantial increase in net profit compared to traditional practices. Therefore, a 0.15% slope is recommended for shorter furrows (50 m), while a gentler 0.05% slope is more suitable for longer furrows (75 m). These findings provide a practical pathway for policymakers and farmers to enhance resource efficiency and contribute to SDG 2 (Zero Hunger) and SDG 6 (Clean Water and Sanitation). Full article

Staphylococcus aureus is a common opportunistic pathogen in rabbits and may cause localized or systemic infections that affect animal health and farm productivity. The present study aimed to investigate the clinical evolution, pathological lesions, and antimicrobial susceptibility profile of S. aureus infections in rabbits raised under traditional extensive systems in Western Romania. A total of 251 rabbits from 11 holdings located in Arad, Timiș, and Caraș-Severin counties were evaluated through epidemiological investigation, clinical examination, necropsy, and bacteriological analysis. Samples were cultured on Brain Heart Infusion medium and 5% sheep blood agar, and isolates were identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Antimicrobial susceptibility testing was performed using an automated system (VITEK 2, bioMérieux) and interpreted according to EUCAST guidelines. Among the examined animals, 68 rabbits (27.1%) showed clinical lesions compatible with S. aureus infection. The most common manifestations included subcutaneous abscesses, otitis externa, rhinitis, mammary abscesses, pyometra, and dental abscesses. Necropsy revealed suppurative and septicemic lesions affecting multiple organs. Antimicrobial susceptibility testing indicated high resistance to penicillin (100%), tetracycline (76.5%), doxycycline (67.6%), and amoxicillin (63.2%). In contrast, florfenicol (69.1% susceptible), ciprofloxacin (61.8%), gentamicin (54.4%), and enrofloxacin (52.9%) showed better antimicrobial activity. The results confirm the clinical and microbiological relevance of S. aureus infections in rabbits raised under traditional conditions and highlight the need for improved biosecurity measures and rational antimicrobial use. Full article

This paper proposes a pig face individual recognition algorithm named DESA-YOLO based on an improved YOLO11 model, aiming to address the adaptability issue of pig face recognition across different growth stages. With the large-scale development of pig farming, traditional individual identification methods suffer from low efficiency and high cost, while pig face recognition technology has great application potential as an important tool for precision suckling and disease prevention. Due to the significant facial feature differences among pigs at different growth stages, this study proposes an improved YOLO11 architecture to address this challenge. The method improves detection accuracy and adaptability by introducing a DualConv structure, an EMA module, a SEAM attention mechanism, and an ASFF detection head. Experimental results show that DESA-YOLO achieves significant improvements over traditional models such as YOLOv5 and YOLOv8 in precision, recall, mAP, and F1 score, obtaining an mAP of 93.7%, which represents increases of 6.3%, 3.5%, and 3% in precision, recall, and mAP respectively compared with the YOLO11 baseline model. Ablation experiments and heatmap visualizations further validate the effectiveness of the proposed improvement modules. The improved model demonstrates higher adaptability and stability across different pig growth stages, while maintaining real-time inference performance for practical deployment. Full article

Despite growing interest in human–animal interactions in livestock, limited information is available on whether differences in routine human contact in the home flock have lasting effects on sheep behaviour and physiological stress responses after transfer to a new herd. This study evaluated behavioural and cortisol related responses in 191 Polish Mountain sheep from five farms that were seasonally combined into one traditional Carpathian grazing flock. Before grazing, farms were classified according to selected characteristics of farmer contact with sheep, including, time spent with the flock, handling style, consistency of interaction, and farmer behaviour towards the animals. Sheep behavioural responses during milking were assessed by the shepherd and an independent observer using a five point scale in two observation periods and serum cortisol concentration was measured in ten sheep per farm. Behavioural scores were analysed using a cumulative link mixed model, with evaluator and season as fixed effects and farm and sheep identity nested within the farm as random effects. Descriptive mean behavioural scores ranged from 2.18 to 4.38, and mean cortisol concentrations ranged from 2.49 to 4.86. Farm level patterns suggested that sheep from farms with more favourable human contact tended to show calmer behaviour during milking and lower cortisol concentrations. These findings indicate that routine human contact in the home flock may be associated with later behavioural and physiological responses under communal grazing conditions. Full article

The sustainable management of intensive swine farming is currently bottlenecked by the difficult valorization of metal-rich wastewater sludge. The structural rigidity of this sludge, stabilized by divalent cation bridging, severely limits its anaerobic digestion and overall resource recovery. To optimize the manure management chain, this study comprehensively evaluated various physical and chemical pretreatments to identify the most effective disintegration strategy for enhanced volatile fatty acid (VFA) production. Among the tested conditions, the coupling of thermal hydrolysis with citrate chelation (T/SC) was the most effective, achieving the highest disintegration degree (12.37%) and biopolymer solubilization. Mechanism analysis revealed that, unlike traditional alkaline treatments, which are limited by the severe reprecipitation of magnesium and phosphate, citrate effectively sequestered bridging cations (Ca²⁺ and Mg²⁺) via ligand exchange. This synergistic disintegration accelerated the fermentation kinetics, enhancing the total VFA yield 2-fold (1293 mg/L) compared to the control group while maintaining a high-value, butyrate-dominant product profile. These findings demonstrate that targeting ionic bridges via ligand-promoted dissolution provides a highly practical and sustainable strategy to maximize resource recovery and nutrient cycling from metal-laden livestock wastes. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

The agriculture sector in Nepal is highly vulnerable to climate change due to its traditional practices, limited technological intervention, and low adaptive capacity. Owing to the country’s complex topography, the impacts of climate change are spatially heterogeneous, making local-level climate change assessments highly relevant. This study focuses on the impact of climate change on three major crops (rice, wheat, and maize), in the Doti district of Nepal, based on meteorological records, crop yield data, questionnaire surveys, and focus group discussions. Climate records from 1982 to 2022 show a trend in annual rainfall at a rate of −3.28 mm per year, with a particularly pronounced decline during the monsoon season. Both maximum and minimum temperatures exhibit statistically significant increasing trends of 0.01 °C and 0.03 °C per year, respectively. The most significant warming for maximum temperature occurs during the monsoon season, while minimum temperature shows the highest increase during the pre-monsoon season. During the same period, annual yields of paddy, maize, and wheat show statistically significant increasing trends. These trends in climate variables and crop yields align with the perceptions of local communities. Linear correlation analysis indicates that maximum and minimum temperatures have a positive influence on crop yields, whereas precipitation and diurnal temperature range have negative effects. Among these, minimum temperature has the greatest impact on crop yields, followed by maximum temperature and rainfall. Multiple linear regression analysis reveals that climate variables better explain long-term trends in crop yields rather than year-to-year variability. The impact of climate is most pronounced in wheat where climate variables account for approximately 55% of the yield variability, followed by paddy (R²~49%) and maize (R²~20%). Despite the overall increase in crop yields, interannual variability has grown, consistent with increased variability in climate parameters. To cope with this uncertainty, local communities have adopted various adaptation strategies, including the use of improved seed varieties, green manure, and changes in crop types. Other key practices include the use of inorganic fertilizers, selection of short-duration crops, crop rotation, minimum tillage farming, and river conservation. Full article

Observing pig behavior, such as feed intake, water intake, and resting behavior, is essential for improving the well-being of these animals. However, monitoring such behaviors by traditional methods can be exhausting for both humans and animals, interfering with their development. The research aimed to identify behavioral patterns of pigs in an air-conditioned environment through computer vision. Microcameras were installed in the animals’ stalls to generate videos over an experimental period of 92 days and the temperature and humidity of the air were simultaneously recorded. The physiological variables of the animals were collected to identify whether they were under heat stress. To recognize the drinking, eating, standing and lying behavior of pigs, YOLOv5 was trained and then the model was used to detect the animals. Regions in the images corresponding to the feeders and drinkers were established. To identify feeding behavior and water intake, criteria based on the occupation of the feeding zone by pigs detected in the standing position were established. The results showed that the trained model achieved an average accuracy rate of 97.3% and an average recall of 96.1% in animal detection. The model exhibited 97.5% accuracy and 97.0% recall rates in recognizing the feeding behavior and water consumption of pigs. The proposed method can be used in videos or images and minimizes the need for manual intervention, offering an efficient means of monitoring pig behavior in agricultural environments and contributing to the productivity of pig farming operations. Full article