Search Results (2,139)

Electrochemical sensing provides an alternative approach for the trace detection of bioactive substances in fruits. However, the complex matrix in fruit tissues, the coexistence of multiple active components, and the varied pH environments limit the sensing performance and accurate quantitative detection of conventional electrochemical sensors. Herein, a dual-mode electrochemical sensor based on a Co₃O₄@N-MWCNTs modified glassy carbon electrode was developed for the sequential detection of quercetin, rutin, and glucose in fruits under acidic and alkaline conditions. The as-prepared electrode exhibited improved charge transfer efficiency and favorable electrocatalytic activity toward the three target analytes. Under optimal conditions, the sensor displayed wide linear ranges of 0.5~70 μM for quercetin and 0.5~5 μM for rutin in acidic environment, with low detection limits of 0.124 μM and 0.045 μM, respectively. In alkaline environment, the detection limit for glucose was determined to be 8.86 μM. Moreover, four combined machine learning models with feature selection algorithms were established, among which the CARS-RFE+RFR model achieved the best prediction accuracy and robustness for multicomponent quantification. Furthermore, the proposed sensing system was applied to the rapid determination of quercetin, rutin, and glucose in real litchi samples, with recoveries ranging from 98.4% to 105.4%. This study provides a feasible electrochemical strategy for multicomponent detection in complex plant matrices, showing good applicability for rapid on-site analysis in agricultural and food-related applications. Full article

Background/Objective: Transitioning towards plant-based dietary patterns is essential to improve health and reduce environmental impact. Hospitals represent a key setting to implement such dietary shifts, yet data on the availability of plant-based meals in healthcare institutions remain scarce. Methods: A cross-sectional survey was conducted across Dutch-speaking hospitals in Belgium to assess the meal plans and whether vegetarian or fully plant-based meal options were available for patients. Besides availability, the frequency and perceived barriers were assessed. Furthermore, the meal plans were analyzed to get an overview of the vegetarian and plant-based food options that were offered in different types of Belgian hospitals. Results: The availability of plant-based meal options was limited across hospitals. No meaningful differences were observed between general hospitals and other hospital types, including psychiatric, rehabilitation, and specialized hospitals. While plant-based fats and oils were widely available, key protein-rich plant foods such as legumes and minimally processed meat alternatives were rarely offered in all types of hospitals. Knowledge gaps among food service staff were observed, and structural barriers—including the need to accommodate diverse dietary requirements—were reported. Conclusions: Belgian hospitals currently underutilize the potential of vegetarian and plant-based nutrition to support patient health and sustainability goals. Strengthening institutional food environments by increasing the availability of nutritionally adequate plant-based meals represents a feasible and impactful strategy to align hospital practice with dietary guidelines and preventive healthcare priorities. Full article

Background/Objectives: Precision nutrition informed by genetic profiling has been proposed to improve public health outcomes; however, long-term, community-based evidence remains limited. This study evaluated the long-term health and economic impacts of the Sakado Folate Project. Methods: Since 2006, residents participating in the Sakado Folate Project received gene-guided nutritional counseling focused on folate intake and related lifestyle factors. Target genes included methylenetetrahydrofolate reductase (MTHFR), angiotensinogen (AGT), adrenoreceptor B3 (ADRB3), and uncoupling protein 1 (UCP1); Δ5-fatty acid desaturase (FADS1) was incorporated later. Biochemical markers, genetic polymorphisms, and health indicators were monitored longitudinally. Population-level health outcomes and per-capita medical expenditure data were compared with regional and national statistics. Results: In program participants (n = 888), folate status and biochemical indicators improved: 76.1% achieved the serum folate target (≥9.5 ng/mL) and 55.3% achieved the serum total homocysteine target (≤7 μmol/L). Healthier lifestyle behaviors were observed across 99,565 Sakado residents, with the city recording the highest proportion of individuals actively attempting lifestyle improvement (31%) of all districts in the region. Disease prevalence was lower in Sakado City than in Saitama Prefecture overall, at standardized prevalence ratios of 52% for stroke and 86% for cerebral infarction. Per-capita medical expenditure was also lower in Sakado City (¥337,800) than the national average (¥392,044) in 2021. Conclusions: Long-term implementation of a community-based, gene-guided nutritional intervention may improve population health outcomes and reduce healthcare expenditures. Integrating nutrigenomics into public health strategies alongside community education and food environment improvements may contribute to sustainable healthcare systems in aging societies. Full article

Accurate prediction of soil organic carbon (SOC) in cropland is important for food production, sustainable soil management, and carbon sequestration. Although digital soil mapping (DSM) has been widely used in the prediction of SOC, most of the current DSM studies use only a single remote sensing image and a single machine learning (ML) approach, and few studies apply multi-temporal remote sensing images and ensemble methods. This study explores the accuracy of the prediction of surface SOC in cropland by comparing multi-temporal Sentinel-2A remote sensing with random forest (RF), support vector machine (SVM), gradient boosted decision trees (GBDT), extreme gradient boosted decision trees (XGBoost), and a stacking ensemble method consisting of these four ML approaches. The potential of multi-temporal remote sensing data and the stacking ensemble method for SOC prediction is discussed. To this end, 76 sampling points were selected in the study area, soil samples were collected at depths of 0–10 cm and 10–20 cm for each soil profile, and a total of 152 soil samples were obtained. Remote sensing variables extracted from topography, climate, and Sentinel-2A images on 13 January and 31 August 2023 were used as predictor variables. The results showed that the stacking ensemble method with multi-temporal predictor variables outperformed all single models and variable combinations. However, the overall predictive accuracy remained moderate, with the best performance for 0–10 cm (R² = 0.386, RMSE = 4.782, MAE = 3.36) and 10–20 cm (R² = 0.425, RMSE = 4.484, MAE = 4.031). The relatively low R² values, despite the use of advanced methods, highlight the inherent challenges of SOC prediction in highly fragmented karst croplands. This study demonstrates the incremental benefit, rather than a universal high accuracy, of combining multi-temporal Sentinel-2 imagery with a stacking ensemble to improve SOC mapping in such complex environments. Full article

Background: Schools are widely recognized as key settings for promoting healthy eating behaviors and supporting childhood obesity prevention. In recent years, increasing attention has been devoted to experiential and interactive nutrition education strategies designed to actively engage children and adolescents in food-related learning processes. These approaches move beyond traditional didactic teaching and include practical and participatory formats, such as cooking activities, school gardening, digital or app-based learning tools, workshops and educational camps, and game-based learning interventions. Objective: This narrative review aims to provide an overview of experiential school-based nutrition education interventions, describing the main types of programs implemented in school settings and summarizing their reported effects on nutrition knowledge, attitudes, and eating behaviors among children and adolescents. Results: Across intervention studies and systematic reviews, hands-on and interactive educational models, including cooking classes, gardening programs, digital learning tools, workshops or camps, and board game-based interventions, frequently report improvements in nutrition knowledge, attitudes toward food, food-related skills, and self-efficacy. These programs seek to strengthen food literacy by combining experiential learning with educational content delivered within the school environment. Evidence regarding changes in dietary intake, diet quality, and anthropometric outcomes is more heterogeneous, with some studies reporting improvements in eating behaviors and others showing more modest or short-term effects. Program outcomes appear to be influenced by several contextual factors, including intervention duration, curriculum integration, teacher involvement, and the availability of resources supporting implementation. Conclusions: Experiential and interactive approaches represent an increasingly adopted strategy in school-based nutrition education. Their effectiveness appears to depend on the quality of implementation, the degree of integration within the school curriculum, and the broader educational context. Future research should further explore how different experiential formats can be optimally integrated into school systems to support the development of food literacy and sustainable healthy eating behaviors among children and adolescents. Full article

Agriculture faces significant challenges from crop diseases, which threaten global food security and cause substantial economic losses annually. While deep learning has advanced plant disease detection, existing models often struggle with generalization across heterogeneous environments and real-time deployment constraints, hindering their practical application in diverse agricultural settings. This paper proposes AD-DETR, an enhanced real-time detection transformer framework specifically designed for agricultural scenarios. The model incorporates three key innovations to address these issues. First, the Multi-Scale Align Network (MSANet) achieves adaptive feature alignment through an Adapt Fusion Align (AFA) block, effectively preserving disease detail information across varying scales. Second, the Spatial–Spectral Attentive Feature Fusion (SSAFF) module integrates frequency-domain processing with attention mechanisms, enhancing feature representation quality by combining spatial and spectral information. Third, the IPIoUv2 loss function improves bounding-box regression accuracy through an internal perception mechanism and scale-adaptive weighting. Comprehensive experiments demonstrate that AD-DETR achieves strong performance, with 90.2% mean average precision at $IoU=0.5$ on the Crop Disease dataset and 97.4% on the PlantDoc dataset. It maintains high efficiency with 16.4 million parameters, 47.2 GFLOPs computational complexity, and inference speeds of 230–242 frames per second. These results indicate that AD-DETR is robust to domain shift and suitable for resource-constrained applications, such as real-time monitoring on mobile and edge platforms. Full article

Hemp (Cannabis sativa L.) is a multipurpose crop with significant industrial and therapeutic potential. This article reviews the various uses of hemp in production, building, food, cosmetics and medicine, focusing on its economic, environmental and health benefits. Industrially, hemp has been used for making fabrics, paper, bioplastics, construction materials and biofuels, because of its strong fibres, fast growth and low impact on the environment. Hemp seed oil and protein in the food and beauty industries are gaining more recognition for their nutritional and functional characteristics. Medically, compounds extracted from hemp, especially cannabidiol (CBD) and other non-psychoactive phytochemicals, have been shown to possess significant anti-inflammatory, pain-relieving, neuroprotective, antioxidant and antibacterial properties. This article talks about how better cultivation methods, processing technologies, and extraction techniques can help improve product quality, marketability, regulatory frameworks, safety standards and the quality control measures that are in place to monitor hemp production and utilization, as well as the focus on new policies in developing nations. Even though hemp has a wide range of potentials, the industry still faces difficulties in the form of laws, lack of infrastructure, unequal product standardization, and lack of scientific proof in certain areas of application. This article further identifies research gaps and points out potential areas for innovation, policymaking, and market development to be explored in the future. If backed up by proper regulations and research, hemp has great potential to contribute to the development of environmentally friendly industries, the improvement of public health and the socio-economic upliftment of communities. Full article

Gene-editing technology provides innovative strategies for coping with crop stress, enhancing resistance to biotic stresses (fungal, bacterial, viral infections) and abiotic stresses (salinity, drought, heavy metals, temperature extremes). The CRISPR/Cas9 system is widely used to knock out susceptibility genes, activate resistance genes, or modulate stress-response genes, yielding many stress-resistant crop varieties. However, off-target effects, chimeric effects, and the complexity of multi-gene synergistic editing limit its application. By optimizing and integrating with other cutting-edge technologies, gene editing is expected to yield highly stress-resistant and high-yielding crop varieties, contributing significantly to sustainable agricultural development and ensuring global food security. Rice, a key staple and model plant, has been extensively studied in gene-editing-based research on stress resistance. The practical potential of gene editing for agricultural improvement has been demonstrated by the effective modification of many genes linked to drought, salinity, temperature extremes, and disease resistance using CRISPR/Cas9 and related technologies. This review discusses gene-editing applications in crop stress research, examining the effects of various stresses on crops and the use of gene editing to develop stress-tolerant varieties. It offers substantial guidance for improving crop stress tolerance through gene editing, creating highly resilient cultivars with greater adaptation to complex, variable environments. Full article

As a major source of investment in Africa, the rapid growth of China’s Foreign Direct Investment (FDI) in Africa has exerted a profound influence on regional development and food security. Based on multinational panel data of African countries from 2006 to 2024, this paper systematically investigates the impact, transmission mechanisms, and heterogeneous characteristics of China’s FDI on food security in Africa. The empirical results show that China’s FDI in Africa has a significant positive effect on food security. Mechanism analysis indicates that China’s FDI improves food security indirectly, mainly through upgrading infrastructure and promoting agricultural technology spillovers. Moderating effect analysis reveals that a sound governance environment and strong absorptive capacity amplify its positive impact, whereas a less diversified industrial structure (a low share of secondary industry) weakens its effectiveness. This paper provides policy implications for optimizing the layout of China’s investment in Africa and promoting the sustainable development of Africa’s food system. Full article

Enhancing crop stress tolerance to ensure global food security is one of the core challenges in agricultural science. Plants predominantly face biotic and abiotic stresses, to which they respond by activating finely regulated signal perception and transduction pathways, thereby improving their survival in adverse environments. The plant cytoskeleton, composed of microtubules and actin filaments, plays a pivotal role in this adaptive process. It functions both as a hub for integrating external stress signals and as a key regulator of downstream signaling and cellular responses. Upon stress, the cytoskeleton undergoes dynamic remodeling, a process driven mainly by microtubule-associated proteins (MAPs) and actin-binding proteins (ABPs). This review systematically summarizes current knowledge on cytoskeletal remodeling in plants under environmental stress, particularly focusing on the functions and mechanisms of MAPs and ABPs in cytoskeletal remodeling. Furthermore, it outlines the regulatory network through which the plant cytoskeleton orchestrates stress adaptation. Full article

Phage therapy has enormous potential in combating bacterial resistance in food animals. However, its application via the oral route remains limited due to challenges associated with the gastrointestinal tract (GIT) environment and a lack of rigorous clinical trial evidence. Therefore, we systematically searched in Google Scholar, PubMed, Scopus, and Web of Science databases following PRISMA guidelines and finally identified 111 articles on oral phage therapy in food animals from where we summarized the key physiological and chemical factors of the gut environment hindering the effectiveness of oral phage therapy (OPT), examined the methods used to evaluate phage stability in the GI environment, and highlighted potential strategies to mitigate these challenges. In addition, we performed quantitative analysis to visualize in vitro pH and thermal stability patterns of phages targeting bacteria isolated from food animals and variability in buffer and incubation period across stability studies. The GIT consists of several anatomically and functionally distinct segments, where complex interactions occur among digestive enzymes, gastric acids, electrolytes, commensal microbiota, and mucosal immune components. The acidic pH of the stomach is a major barrier to successful oral phage delivery. According to our analysis of pH stability testing data from the reviewed studies, most phages targeting antimicrobial-resistant bacteria in food animals remained stable at pH 5–9 and inactivated under highly acidic (pH ≤ 2) or highly alkaline (pH ≥ 11) conditions. In addition, phages are susceptible to high temperatures (above 60 °C), digestive enzymes (e.g., pepsin, trypsin, lipases), bile salts, and host immune responses. Several in vitro laboratory techniques are available to assess phage stability under simulated GI conditions, but variations occur in the assessment protocols. Microencapsulation using alginate and chitosan has been used to protect phages from the adverse GI environment. Additionally, enteric-coated capsules, antacids, co-encapsulation with acid-neutralizing agents, consumption of alkaline water, and daily phage administration are suggested to improve phage survival and efficacy. For the successful clinical implementation of OPT in food animals, future research should focus on elucidating the molecular and physicochemical determinants of phage stability, understanding the humoral immune response to OPT, standardizing laboratory protocol for assessing phage viability, improving the scalability of encapsulation methods, and exploring other potential delivery techniques. Full article

Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP_50–95, mAP₅₀, and mAP₇₅, respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP₅₀ over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture. Full article

Tomato leaf diseases represent a persistent threat to global food security, causing annual crop losses of 20% to 40%. Although deep learning models achieve accuracies exceeding 95% in centralized settings, their deployment across distributed farms is constrained by data privacy concerns, communication bottlenecks, and heterogeneous data quality. This paper proposes Personalized, Clustered, and Communication-Efficient Federated Learning (PCE-FL), a framework that integrates three synergistic components: (1) server-side client clustering to group farms with similar data distributions for personalized model training; (2) federated knowledge distillation to reduce communication overhead by over 91%; and (3) reputation-based aggregation to ensure robustness against unreliable contributions. Extensive experiments on realistic non-IID simulations of the PlantVillage tomato dataset Dirichlet($\alpha \in \{1.0,0.5,0.1\}$) demonstrate that PCE-FL achieves 89.1% accuracy under extreme heterogeneity ($\alpha =0.1$), surpassing FedAvg by 10.9 and IFCA by 4.8 percentage points, while maintaining a 91% reduction in communication cost. All improvements are statistically significant ($p<0.001$). These results advance the practical deployment of privacy-preserving collaborative AI in resource-constrained agricultural environments. Full article

Soil degradation in both agricultural and urban environments is accelerating due to intensive land use, plastic pollution, construction practices, and climate change, threatening ecosystem stability, food security, and carbon storage capacity. This review synthesizes current advances in biopolymeric materials as regenerative alternatives to conventional soil management approaches. Biopolymers derived from natural sources—including polysaccharides, proteins, and lignin-based compounds—are examined for their multifunctional roles in improving soil structure, enhancing water retention, optimizing nutrient delivery, stabilizing slopes, and supporting pollutant immobilization. Recent developments highlight the emergence of stimuli-responsive hydrogels, controlled-release fertilizer matrices, and composite soil conditioners capable of simultaneously addressing water stress, salinity, erosion, and contamination. In parallel, biodegradable agricultural films and in-soil degradable materials offer pathways to reduce microplastic accumulation while maintaining agronomic performance. Beyond agriculture, bio-based construction materials and bio-receptive design strategies extend biopolymeric interventions into the built environment, promoting soil permeability, microbial diversity, and circular material flows. The review emphasizes the need for context-specific formulation, long-term field validation, and life-cycle assessment to ensure environmental safety and scalability. By integrating soil science, polymer chemistry, and regenerative design, biopolymeric systems are described here as tools for restoring soil health and fostering resilient urban–rural ecosystems under conditions of environmental change. 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