Search Results (2,372)

Enhancing egg production in geese without antibiotics remains a challenge in poultry science. This study compared the effects of Lactobacillus (LAB) and Bacillus (BAC) probiotics on laying performance, gut microbiota, and serum metabolism in Zhedong White geese. Birds were fed a control diet or diets supplemented with LAB or BAC. Egg production and quality were monitored throughout the trial. Serum metabolomics and fecal 16S rRNA sequencing were integrated with KEGG enrichment and correlation analyses to uncover functional mechanisms. Both probiotics improved laying performance and egg quality. Total egg production of the LAB group was 8.5% higher than that of the BAC group (p < 0.05). The LAB group’s advantage in egg production was consistent with its stronger activation of the steroid hormone biosynthesis pathway (elevated serum corticosterone and tetrahydrocorticosterone indicated an overall enhancement of steroidogenic flux). Simultaneously, the LAB group exhibited a more efficient conversion of L-phenylalanine to catecholamine precursors, which drove activation of the neuroendocrine reproductive axis. The BAC group showed more significant changes in nitrogen and energy metabolism pathways and a more pronounced expansion of energy-harvesting Firmicutes. These findings reveal two strain-specific regulatory pathways: LAB functions through the “aromatic amino acid–neuroendocrine–steroid hormone axis,” while BAC relies on the “gut microbiota–energy metabolism” pathway, with direct implications for the precise application of probiotics under antibiotic-free farming conditions. Full article

Intensive pig farming presents substantial challenges for individual health monitoring due to high stocking densities, complex occlusion scenarios, and the need for continuous real-time surveillance. Existing monitoring approaches rely heavily on manual inspection, which is labor-intensive and prone to delayed detection of abnormal behaviors and disease symptoms. This study proposes an embedded intelligent monitoring system integrating a pan-tilt gimbal platform with an improved multi-object tracking and anomaly detection framework for automated pig health surveillance. The system employs a modified Periodfill_DeepSORT algorithm that incorporates a ReID network with appearance features and motion prediction trajectories to maintain identity consistency under occlusion and re-entry scenarios. For anomaly detection, a lightweight YOLOv8-based network was trained on 772 abnormal samples across three behavioral categories: movement abnormalities, postural abnormalities, and disease-related abnormalities. Experimental results demonstrate that the Periodfill_DeepSORT algorithm achieves a Multiple Object Tracking Accuracy (MOTA) of 95.34%, a Multiple Object Tracking Precision (MOTP) of 94.77%, and an IDF1 score of 96.88%, with only 12 identity switches across 2000 frames involving 12 targets—27 fewer than the standard DeepSORT algorithm. In occlusion scenarios, MOTA improved from 61.1% to 78.3%. The anomaly detection network achieves an overall detection accuracy of 94.5%, representing an 8.8 percentage point improvement over the baseline model, with recognition accuracies of 96.2% for movement abnormalities, 94.1% for postural abnormalities, and 92.8% for disease-related abnormalities. The system operates at 90 frames per second on embedded hardware with a power consumption of 3.2 watts and a startup time of approximately 1 s, with gimbal angle errors maintained within 3°. These results demonstrate the system’s effectiveness and practical feasibility for real-time intelligent health monitoring in intensive livestock farming environments. Full article

Accurate field-scale wheat yield estimation is essential for precision agriculture, farm-level decision-making, and food security planning. However, operational studies conducted under real commercial farming conditions in the eastern Mediterranean remain limited. This study investigated whether multi-temporal Sentinel-2 imagery could support reliable wheat yield estimation across nine commercial wheat fields near Ptolemaida, Greece, during the 2023–2024 growing season. Both durum and common wheat fields were included, and combine-harvester yield maps were used as ground-truth observations. Six regression algorithms—the Random Forest (RF), Support Vector Regression (SVR), k-nearest neighbors (KNN), Decision Tree (DT), LASSO regression, and Gaussian Process Regression (GPR) algorithms—were evaluated using three feature configurations: raw Sentinel-2 spectral bands only (Sentinel-only (SO)), spectral bands combined with vegetation indices (Sentinel+Indices, SI), and vegetation indices only (Indices-only, IO). Model generalization was assessed through a strict Leave-One-Field-Out (LOFO) cross-validation protocol, and the method of SHapley Additive exPlanations (SHAP) was used to interpret model behavior and identify the most influential spectral regions and phenological stages. RF achieved the highest predictive accuracy, with a MAPE of 7.90% and an RMSE of 45.15 kg decare⁻¹ under the SO configuration, demonstrating a statistically significant improvement over DT and KNN models ($p<0.05$). SHAP analysis indicated that model predictions were mainly driven by SWIR-1, NIR-narrow, and red-edge bands acquired during late grain filling and maturity, while vegetation indices contributed limited additional information. These findings suggest that raw multi-temporal Sentinel-2 spectral bands are highly effective for field-scale wheat yield estimation within the scope of this study, although further validation across diverse growing seasons and geographic regions is required to confirm broad operational sufficiency. Full article

Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. 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

Agricultural soils are a major source of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄), highlighting the need for cost-effective and field-applicable monitoring solutions. This study developed and evaluated a low-cost real-time monitoring system for soil CO₂ and CH₄ emissions by integrating surface emission chambers, low-cost gas sensors, a solar-powered energy supply, and IoT-based wireless communication. Three acrylic chambers with different heights (40, 60, and 80 cm) were fabricated to investigate the influence of chamber geometry on measurement performance. System performance was assessed through simultaneous measurements against a Biogas 5000 analyzer under simulated conditions and during field deployment in a sugarcane cultivation area in Khon Kaen Province, Thailand. Relative agreement was used to compare the developed system with the reference instrument. The results showed that relative agreement varied with chamber height for both gases. Under simulated conditions, the 80 cm chamber achieved the highest overall relative agreement for CO₂ and CH₄, underscoring the importance of sufficient headspace volume in chamber-based measurements. Field experiments confirmed the system’s capability for continuous CO₂ monitoring in an agricultural environment. However, CH₄ emissions were not detected during the study period, likely due to drought-induced, well-aerated soil conditions. The developed system demonstrated stable autonomous operation, low energy consumption, and ease of installation, making it suitable for long-term field applications. Overall, the proposed platform provides a practical and scalable approach for real-time soil GHG monitoring and offers strong potential for integration into precision agriculture and climate-smart farming systems to support GHG mitigation strategies. Full article

Microgreens have emerged as a nutrient-dense specialty crop with great potential to address global nutritional challenges through urban farming and controlled-environment agriculture. While interest in enhancing both the yield and nutritional quality of hydroponic microgreens is growing, a comprehensive synthesis of horticultural strategies is still lacking. This gap hinders the development of integrated approaches needed for efficient and targeted quality improvement. This review systematically examines the current literature on horticultural interventions for improving hydroponic microgreen production, focusing on nutrient solution management, light environmental manipulation, substrate selection, genetic potential, and emerging synergistic approaches. Nutrient solution optimization, including appropriate concentration, timing, and targeted biofortification with essential elements, enhances both productivity and nutritional density. Light spectral manipulation, particularly through red-to-far-red ratios or blue-light supplementation, enables precise control of morphology and the accumulation of bioactive compounds. Substrate physicochemical properties influence nutrient availability and uptake, while genetic variability among species and cultivars provides the foundation for biofortification efforts. Emerging approaches including biostimulant application, integrated pre- and post-harvest practices, and phenotyping and artificial intelligence integration offer additional avenues for sustainable quality enhancement. This review provides a framework for optimizing hydroponic microgreen production systems to simultaneously achieve high yield and enhanced nutritional quality. Full article

Avibacterium paragallinarum (A. paragallinarum) is the primary causative agent of infectious coryza in chickens. Infection often leads to growth retardation in broilers and a 10% reduction in egg production, reaching over 40% in laying hens. The problem is particularly severe under intensive farming conditions, significantly jeopardizing global poultry health and farming profitability. From a ‘One Health’ perspective, this not only disrupts the stability of the food supply chain, but also increases antibiotic usage due to disease prevention and control needs, thereby aggravating antimicrobial resistance (AMR) and posing a global public health challenge. This review systematically summarizes advances in the pathogenesis of A. paragallinarum and the protective immunity induced by subunit vaccines. It focuses on the infection mechanisms of A. paragallinarum, emphasizing its colonization strategies in the infraorbital sinus and nasal epithelium of chickens, and analyzes the roles of key virulence factors such as hemagglutinin and capsule in adhesion, colonization, and immune evasion. We integrate the tissue-specific pathogenesis of A. paragallinarum with the role of respiratory commensal microbiota in facilitating infection, providing an in-depth analysis of the bacterium’s key immune evasion strategies, thus offering novel insights into host–pathogen-microbiome interactions. Concurrently, to the best of our knowledge, this review provides the first comprehensive overview of current developments in subunit vaccines and their immunoprotective properties, with special attention to limitations in eliciting mucosal immune responses. By delving into the pathogen-host interaction mechanisms, this review aims to inform the optimization of subunit vaccine design and immunization strategies. Ultimately, it seeks to establish a theoretical basis and practical framework for precise control of A. paragallinarum. Full article

Improving animal welfare through standardized management protocols remains a key challenge in intensive pig production. Auditory enrichment, such as music, represents a promising non-invasive strategy, yet its application is often empirical, lacking mechanistic understanding and objective assessment tools. This study investigated growing pigs’ active preferences for structured musical parameters to establish a precision auditory enrichment framework. Seventy-two crossbred pigs were subjected to a free-choice paradigm under simulated farm conditions, with a 2 × 2 factorial design manipulating musical stimulus type (a guqin string piece vs. a Mozart wind excerpt) and tempo (fast: 200 bpm vs. slow: 65 bpm) was continuously quantified using an enhanced YOLOv11-based automated recognition system (mean average precision mAP₅₀: 90.5% ± 1.5%). Results revealed highly parameter-dependent effects: the slow-tempo GS stimulus and the fast-tempo MF stimulus significantly prolonged occupancy time (p < 0.01) and elicited distinct profiles. The GS stimulus promoted a calm, investigative state, increasing lying, exploration, and drinking time (p < 0.05), while the MF stimulus stimulated an active playful state, characterized by increased walking and playing (p < 0.05). Other musical combinations showed negligible effects, whereas noise exposure consistently triggered stress-related responses. This study establishes an integrated “parametric design → automated assessment → specific output” methodology for precision auditory enrichment, providing an empirical basis for evidence-based acoustic protocols in commercial pig production. Full article

Despite producing much of the world’s food, small-scale farms face severe resource shortages, climate risks, and infrastructure gaps. While digital advances ranging from IoT sensing to AI-driven analytics offer pathways to improve productivity, adoption remains uneven. This integrative review synthesizes evidence on smart-farming technologies specifically for smallholders, identifying primary barriers, enabling conditions, and design principles for successful deployment. Unlike broader smart-farming reviews, the article explicitly evaluates small-farm suitability, evidence quality, and implementation architecture rather than technological capability alone. The synthesis shows that adoption is consistently constrained by clustered barriers, notably high capital and maintenance costs, limited technical capacity, and unreliable electricity or internet access. It also finds that evidence is strongest for modular, offline-capable monitoring and alerting tools, while evidence for durable gains from highly integrated full-platform systems remains thinner and more pilot-dependent. To advance equitable innovation, the review proposes a fit-for-context deployment logic centered on co-design, local repair and advisory capacity, and financing and policy support aligned with small-farm realities. Overall, smart farming can strengthen productivity, resilience, and environmental performance on small farms, but only when technologies are embedded in inclusive service models and implementation systems. 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

Although lightweight deep learning models have shown promise for livestock monitoring, there is still limited evidence regarding their comparative performance and practical deployment under real broiler production conditions characterized by high stocking density, severe occlusion, and constrained computational resources. In this context, the present study aimed to evaluate three lightweight object detection architectures for broiler monitoring and to determine their suitability for low-cost edge deployment in settings relevant to small and medium-sized producers. A novel dataset, publicly released through Zenodo to support reproducibility, was constructed from images acquired in both a prototype farm and a high-density commercial facility. These environments captured the visual complexity of intensive broiler production, where overlapping individuals and frequent occlusion challenge detection performance. YOLOv10s, Faster R-CNN, and EfficientDet-D0 were trained and evaluated for detection accuracy and computational efficiency. YOLOv10s achieved the best results, with a mean Average Precision (mAP) of 0.95, whereas Faster R-CNN and EfficientDet-D0 were less suitable for crowded scenes due to region proposal saturation and limited feature-extraction capacity. The selected model was further implemented on a Raspberry Pi 5, achieving a stable latency of 392.17 ms. These results demonstrate that YOLOv10s provides a robust balance between accuracy and efficiency for local broiler monitoring on affordable hardware, while also indicating that active thermal management is necessary to maintain operational stability under real-world conditions. Full article

Accurately predicting crop yield and its spatiotemporal variability is crucial for precision agriculture. This study developed a prior knowledge-guided remote sensing yield estimation framework at Youyi Farm in China. Based on multi-source data from 2016 to 2025, a Yield-Formation Key Dataset (YFKD) was constructed by integrating Meteorological, Eco-physiological, Phenological, and Soil features. Combined with Boruta feature selection, MLR (Multiple Linear Regression), RF (Random Forest), and XGBoost (Extreme Gradient Boosting) models were compared, and SHAP (Shapley Additive Explanations) was utilized for spatiotemporal driving force analysis. The results showed that the YFKD-XGBoost model achieved the optimal performance ($R^2=0.865$, RMSE = 1491 kg/ha), improving accuracy by up to 17.7% compared to the baseline model. Global SHAP analysis revealed that Soil Spectral Reflectance provided the highest contribution. Temporally, the period from late July to mid-September (especially mid-August) served as the critical monitoring window. Spatially, based on the area share of the dominant negative SHAP contributor, Meteorological Background was the most widespread limiting factor (34.8% of the constrained area), Soil Conditions constraints showed localized clustering (16.4%), while Phenological and Eco-physiological constraints dominated intra-field spatial differentiation. This study validated the feasibility of this framework for high-precision yield estimation and the analysis of yield formation driving factors under the constraints of a limited regional dataset (n = 233), providing reliable support for regional differentiated agricultural management. Full article

Precision monitoring of crops using drone or unmanned aerial vehicle (UAV) technology is rapidly growing as a climate-smart agriculture practice in rice farming systems in Sri Lanka and globally. In rice fields, the Leaf Color Chart (LCC) is traditionally used for manual comparison of a leaf to the standard LCC categories in the field to determine the fertilizer condition of the plant. However, this lacks autonomous monitoring, rapid monitoring of larger fields, scalability, and the digital transformation of the scores with sprayer drones for targeted fertilizer application. Drones with multispectral cameras could pose a greater rapid and digitalized solution for delineation of leaf color instead of LCC, in the field. Thus, this paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms. The experimental setup consisted of ground sampling of LCC levels 3 and 4 from farmer fields and acquisition of drone imagery data above the field with a DJI Phantom 4 Multispectral UAV, from which fifteen vegetation indices related to crop spectra were extracted. The vegetation indices were then employed for training (70%) and testing (30%) with machine learning algorithms: Random Forest (RF), as well as SVM-linear and SVM-RBF, focusing on LCC 3–4 class classification. The results showed good classification performance, with the RF algorithm reporting a test accuracy of 98.2%, outperforming SVM-linear (82.5%) and SVM-RBF (87.5%). The RF model outputs SR, EVI, MSR, NDVI, and TCARI as feature importance indices for the classification of LCC levels 3 and 4 in the rice field. The findings of this proposed method greatly encourage the adaptation of drone technology for real-time monitoring of rice leaf fertilizer levels linked to LCC levels three and four, and spatial identification of the zones across the field. This imposes greater advancement towards climate-smart rice cultivation, targeted fertilizer application and rice field landscape pattern change analysis, underpinning the importance of field digitization. Full article

Precision Livestock Farming (PLF) has emerged as an approach in modern animal production, integrating advanced technologies such as sensors, automation, data analytics, and artificial intelligence to enable continuous, individualised monitoring of livestock and their environment. This review examines the impact of PLF technologies on three critical dimensions of livestock systems: productivity, animal welfare, and environmental sustainability. PLF applications, including wearable and environmental sensors, automated feeding and milking systems, and video-based monitoring, allow for early detection of health and behavioural deviations, optimisation of feed efficiency, and improved reproductive and disease management. These technologies support proactive, data-driven decision-making that enhances productivity while promoting animal welfare and reducing the environmental footprint of livestock production. Despite these benefits, the adoption of PLF faces significant challenges, including high initial investment costs, technical limitations, system integration issues, data ownership and privacy concerns, and ethical considerations related to automation. Future research and policy efforts should focus on developing cost-effective, scalable solutions, standardised data frameworks, and supportive regulatory measures to enable equitable and responsible implementation across diverse production systems. By addressing these challenges, PLF offers a pathway towards more efficient, welfare-oriented, and environmentally sustainable livestock production, contributing to global food security and resilient agricultural systems. Full article