Search Results (338)

The inclusion of the Internet of Things (IoT) in indoor agricultural systems has become a fundamental tool for improving cultivation systems by providing key information for decision-making in pursuit of better performance. This article presents the design and implementation of an IoT-based agricultural system installed in a plant growth chamber for hydroponic cultivation under controlled conditions. The growth chamber is equipped with sensors for air temperature, relative humidity (RH), carbon dioxide (CO₂) and photosynthetically active photon flux, as well as control mechanisms such as humidifiers, full-spectrum Light Emitting Diode (LED) lamps, mini split air conditioner, pumps, a Wi-Fi surveillance camera, remote monitoring via a web application and three Nutrient Film Technique (NFT) hydroponic systems with a capacity of ten plants each. An ATmega2560 microcontroller manages the smart system using the MODBUS RS-485 communication protocol. To validate the proper functionality of the proposed system, a case study was conducted using lettuce crops, in which the impact of different nutrient solution concentrations (50%, 75% and 100%) on the phenotypic development and nutritional content of the plants was evaluated. The results obtained from the cultivation experiment, analyzed through analysis of variance (ANOVA), show that the treatment with 75% nutrient concentration provides an appropriate balance between resource use and nutritional quality, without affecting the chlorophyll content. This system represents a scalable and replicable alternative for protected agriculture. Full article

Grapevine (Vitis vinifera L.) cultivation is an important agricultural sector worldwide. Its expansion into new areas, like Kazakhstan, brings significant phytosanitary risks. Viroids, such as grapevine yellow speckle viroid 1 (GYSVd-1) and hop stunt viroid (HSVd), are RNA pathogens that threaten vineyard productivity. They can cause a progressive decline through latent infections. Traditional diagnostic methods are usually targeted and therefore not suitable for thorough surveillance. In contrast, modern high-throughput sequencing (HTS) methods often face challenges due to their high costs and complicated sample preparation, such as ribosomal RNA (rRNA) depletion. This study introduces a simplified diagnostic workflow that overcomes these barriers. We utilized the latest Oxford Nanopore V14 cDNA chemistry, which is designed to prevent internal priming, by substituting a targeted oligo(dT)VN priming strategy to facilitate the sequencing of non-polyadenylated viroids from total RNA extracts, completely bypassing the rRNA depletion step and use of random oligonucleotides for c DNA synthesis. This method effectively detects and identifies both GYSVd-1 and HSVd. This workflow significantly reduces the time, cost, and complexity of HTS-based diagnostics. It provides a powerful and scalable tool for establishing strong genomic surveillance and phytosanitary certification programs, which are essential for supporting the growing viticulture industry in Kazakhstan. Full article

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article

Invasive fungal infections (IFIs) represent a growing global health threat, particularly for immunocompromised populations, with mortality exceeding 1.5 million deaths annually. Despite their clinical and economic burden—costing billions in healthcare expenditures—fungal infections remain underprioritized in public health agendas. This review examines the current landscape of antifungal therapy, focusing on advances, challenges, and future directions. Key drug classes (polyenes, azoles, echinocandins, and novel agents) are analyzed for their mechanisms of action, pharmacokinetics, and clinical applications, alongside emerging resistance patterns in pathogens like Candida auris and azole-resistant Aspergillus fumigatus. The rise of resistance, driven by agricultural fungicide use and nosocomial transmission, underscores the need for innovative antifungals, rapid diagnostics, and stewardship programs. Promising developments include next-generation echinocandins (e.g., rezafungin), triterpenoids (ibrexafungerp), and orotomides (olorofim), which target resistant strains and offer improved safety profiles. The review also highlights the critical role of “One Health” strategies to mitigate environmental and clinical resistance. Future success hinges on multidisciplinary collaboration, enhanced surveillance, and accelerated drug development to address unmet needs in antifungal therapy. Full article

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehensive review systematically analyzes many seminal studies to evaluate cutting-edge DL applications across three core domains: contaminant surveillance (heavy metals, pesticides, and mycotoxins), nutritional component quantification (soluble solids, polyphenols, and pigments), and structural/biomarker assessment (disease symptoms, gel properties, and physiological traits). Emerging hybrid architectures—including attention-enhanced convolutional neural networks (CNNs) for lesion localization, wavelet-coupled autoencoders for spectral denoising, and multi-task learning frameworks for joint parameter prediction—demonstrate unprecedented accuracy in decoding complex agricultural matrices. Particularly noteworthy are sensor fusion strategies integrating hyperspectral imaging (HSI), Raman spectroscopy, and microwave detection with deep feature extraction, achieving industrial-grade performance ($RPD$ > 3.0) while reducing detection time by 30–100× versus conventional methods. Nevertheless, persistent barriers in the “black-box” nature of complex models, severe lack of standardized data and protocols, computational inefficiency, and poor field robustness hinder the reliable deployment and adoption of DL for detecting special components in agricultural products. This review provides an essential foundation and roadmap for future research to bridge the gap between laboratory DL models and their effective, trusted application in real-world agricultural settings. Full article

Antimicrobial resistance (AMR) has emerged as a planetary health emergency, driven not only by the clinical misuse of antibiotics but also by diverse environmental dissemination pathways. This review critically examines the role of environmental compartments—water, soil, and air—as dynamic reservoirs and transmission routes for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). Recent metagenomic, epidemiological, and mechanistic evidence demonstrates that anthropogenic pressures—including pharmaceutical effluents, agricultural runoff, untreated sewage, and airborne emissions—amplify resistance evolution and interspecies gene transfer via horizontal gene transfer mechanisms, biofilms, and mobile genetic elements. Importantly, it is not only highly polluted rivers such as the Ganges that contribute to the spread of AMR; even low concentrations of antibiotics and their metabolites, formed during or after treatment, can significantly promote the selection and dissemination of resistance. Environmental hotspots such as European agricultural soils and airborne particulate zones near wastewater treatment plants further illustrate the complexity and global scope of pollution-driven AMR. The synergistic roles of co-selective agents, including heavy metals, disinfectants, and microplastics, are highlighted for their impact in exacerbating resistance gene propagation across ecological and geographical boundaries. The efficacy and limitations of current mitigation strategies, including advanced wastewater treatments, thermophilic composting, biosensor-based surveillance, and emerging regulatory frameworks, are evaluated. By integrating a One Health perspective, this review underscores the imperative of including environmental considerations in global AMR containment policies and proposes a multidisciplinary roadmap to mitigate resistance spread across interconnected human, animal, and environmental domains. Full article

Inconsistency in the structural strengths of a membrane wing under positive and negative loads has undesirable impacts on the aeroelastic deflections of the wing, which results in more significant flight control system modeling errors and worsens the performance of the aircraft. In this paper, an integrated dynamic model is derived for a membrane-wing aircraft based on the structural dynamics equation of the membrane wing and the flight dynamics equation of the traditional fixed wing. Based on state feedback control theory, an autopilot system is designed to unify the flight and control properties of different flight and wing deformation statuses. The system uses models of different operating regions to estimate the dynamic response of the vehicle and compares the estimation results with the sensor signals. Based on the compared results, the autopilot can identify the overall flight and select the correct operating region for the control system. By switching to the operating region with the minimum modeling error, the autopilot system maintains good flight performance while flying in turbulence. According to the simulation results, compared with traditional rigid aircraft autopilots, the proposed autopilot can reduce the absolute maximum attack angles by nearly 27% and the absolute maximum wingtip twist angles by nearly 25% under gust conditions. This enhanced robustness and stability performance demonstrates the autopilot’s significant potential for practical deployment in micro-aerial vehicles, particularly in applications demanding reliable operation under turbulent conditions, such as military surveillance, environmental monitoring, precision agriculture, or infrastructure inspection. Full article

The agriculture sector is increasingly challenged to maintain productivity and sustainability amidst environmental, marketplace, and geopolitical pressures. While precision agriculture enhances physical production, the financial resilience of agricultural firms has been understudied. In this study, machine learning (ML) methods, including logistic regression (LR), decision trees (DTs), and artificial neural networks (ANNs), are employed to predict the bankruptcy risk for Central and Eastern European (CEE) farming firms. All models consistently showed high performance, with AUC values exceeding 0.95. DTs had the highest overall accuracy (95.72%) and F1 score (0.9768), LR had the highest recall (0.9923), and ANNs had the highest discrimination power (AUC = 0.960). Visegrad, Balkan, Baltic, and Eastern Europe subregional models featured economic and structural heterogeneity, reflecting the need for local financial risk surveillance. The results support the development of AI-based early warning systems for agricultural finance, enabling smarter decision-making, regional adaptation, and enhanced sustainability in the sector. Full article

Herbicide phytotoxicity represented a critical constraint on crop safety in soybean–corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386–1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518–690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation. Full article

Gastrointestinal (GI) parasites not only significantly impact goat health and productivity but can also affect human health due to the zoonotic potential of some species. This study investigates the prevalence of internal parasites within the tropical monsoon ecosystem of southern Thailand, focusing on both phenotypic and molecular characteristics of the parasites and identifying associated risk factors in caprine farming systems. A total of 276 meat goats from Nakhon Si Thammarat province were examined, indicating an overall GI parasite prevalence of 88.8% (245/276), with strongyles and Eimeria spp. identified as the dominant parasites. In addition, mixed parasitic infections were observed in 72.2% of cases, whereas single infections comprised 27.8%. Strongyle-positive fecal samples were cultured and genetically sequenced, revealing the presence of Haemonchus contortus, Trichostrongylus colubriformis, and Oesophagostomum asperum. For associated risk factors, gender and grazing with other herds significantly impacted overall GI parasitic infections, while the gender, breed, and packed cell volume (PCV) affected the strongyle infection. A correlation analysis revealed a substantial relationship between strongyle egg per gram (EPG) counts and clinical parameters, indicating that monitoring animals with low body condition scores (BCS) and high Faffa Malan Chart (FAMACHA) scores could be an effective strategy for controlling strongyle infections. These findings highlight the importance of continued research and effective farm management practices to address strongyle infections in meat goats, improving their health and agricultural productivity in tropical regions. Moreover, the detection of four zoonotic parasites (Giardia spp., H. contortus, T. colubriformis, and Fasciola spp.) indicates the necessity for the routine surveillance and monitoring of zoonotic parasites in goats to mitigate potential human health risks. Full article

Antimicrobial resistance (AMR) poses a growing threat to global health, and its spread through the food chain is gaining increasing attention. While AMR in food of animal origin has been extensively studied, less is known about its prevalence in plant-based foods, particularly fresh and ready-to-eat (RTE) vegetables. This study investigated the occurrence of antimicrobial-resistant bacteria in fresh and RTE vegetables. Isolates were subjected to antimicrobial susceptibility testing and molecular analyses for the characterization of antimicrobial resistance genes (ARGs). A significant proportion of samples were found to harbor antimicrobial-resistant bacteria, including multidrug-resistant strains. Several ARGs, including those encoding extended-spectrum β-lactamases (ESBLs) and resistance to critically important antimicrobials, were detected. The findings point to environmental contamination—potentially originating from wastewater reuse and agricultural practices—as a likely contributor to AMR dissemination in vegetables. The presence of antimicrobial-resistant bacteria and ARGs in fresh produce raises concerns about food safety and public health. The current regulatory framework lacks specific criteria for monitoring AMR in vegetables, highlighting the urgent need for surveillance programs and risk mitigation strategies. This study contributes to a better understanding of AMR in the plant-based food sector and supports the implementation of a One Health approach to address this issue. Full article

Since 2020, the Gs/Gd H5N1 influenza virus (clade 2.3.4.4b) has established itself within wild bird populations across Asia, Europe, and the Americas, causing outbreaks in wild mammals, commercial poultry, and dairy farms. The impacts on the bird populations and the agricultural industry has been significant, requiring a One Health approach to enhanced surveillance in both humans and animals. To support pandemic preparedness efforts, we evaluated the Cepheid Xpert Xpress CoV-2/Flu/RSV plus kit and the BioFire Respiratory 2.1 Panel for their ability to detect the presence of non-seasonal, zoonotic influenza A viruses, including circulating H5N1 viruses from clade 2.3.4.4b. Both assays effectively detected the presence of influenza virus in clinically-contrived nasal swab and saliva specimens at low concentrations. The results generated using the Cepheid Xpert Xpress CoV-2/Flu/RSV plus kit and the BioFire Respiratory 2.1 Panel, in conjunction with clinical and epidemiological findings provide valuable diagnostic findings that can strengthen pandemic preparedness and surveillance initiatives. Full article

The intersection of microbial food safety and antimicrobial resistance (AMR) represents a mounting global threat with profound implications for public health, food safety, and sustainable development. This review explores the complex pathways through which foodborne pathogens—such as Salmonella spp., Escherichia coli (E. coli), Listeria monocytogenes (L. monocytogenes), and Campylobacter spp.—acquire and disseminate resistance within human, animal, and environmental ecosystems. Emphasizing a One Health framework, we examine the drivers of AMR across sectors, including the misuse of antibiotics in agriculture, aquaculture, and clinical settings, and assess the role of environmental reservoirs in sustaining and amplifying resistance genes. We further discuss the evolution of surveillance systems, regulatory policies, and antimicrobial stewardship programs (ASPs) designed to mitigate resistance across the food chain. Innovations in next-generation sequencing, metagenomics, and targeted therapeutics such as bacteriophage therapy, antimicrobial peptides (AMPs), and CRISPR-based interventions offer promising alternatives to conventional antibiotics. However, the translation of these advances into practice remains uneven, particularly in low- and middle-income countries (LMICs) facing significant barriers to diagnostic access, laboratory capacity, and equitable treatment availability. Our analysis underscores the urgent need for integrated, cross-sectoral action—anchored in science, policy, and education—to curb the global spread of AMR. Strengthening surveillance, investing in research, promoting responsible antimicrobial use, and fostering global collaboration are essential to preserving the efficacy of existing treatments and ensuring the microbiological safety of food systems worldwide. Full article

With the increased proliferation of drone use for many purposes, counter drone technology has become crucial. This rapid expansion has inherently introduced significant opportunities and applications. This creates applications such as aerial surveillance, delivery services, agriculture monitoring, and, most importantly, security operations. Due to the relative simplicity of learning and operating a small-scale UAV, malicious organizations can field and use UAVs (drones) to form substantial threats. Their interception may then be hindered by evasive manoeuvres performed by the malicious UAV (mUAV). Novice operators may also unintentionally fly UAVs into restricted airspace such as civilian airports, posing a hazard to other air operations. This paper explores predictive trajectory code and methods for the neutralisation of mUAVs by following drones, using state estimation techniques such as the extended Kalman filter (EKF) and particle filter (PF). Interception strategies and optimization techniques are analysed to improve interception efficiency and robustness. The novelty introduced by this paper is the implementation of adaptive time horizon (ATH) and velocity control (VC) in the predictive process. Simulations in MATLAB were used to evaluate the effectiveness of trajectory prediction models and interception strategies against evasive manoeuvres. The tests discussed in this paper then demonstrated the following: the EKF predictive method achieved a significantly higher neutralisation rate (41%) compared to the PF method (30%) in linear trajectory scenarios, and a similar neutralisation rate of 5% in stochastic trajectory scenarios. Later, after incorporating adaptive time horizon (ATH) and 20 velocity control (VC) measures, the EKF method achieved a 98% neutralization rate, demonstrating significant improvement in performance. Full article

Phytoplasmas are wall-less, phloem-restricted bacteria responsible for numerous significant plant diseases worldwide. An increasing body of evidence indicates that phytoplasmas can coexist with other pathogens in mixed infections, including various 16Sr group phytoplasmas, ‘Candidatus Liberibacter’ species, viruses, spiroplasmas, fungi, and other difficult-to-culture phloem-limited bacteria. These interactions challenge established views regarding the causes, detection, and management of plant diseases. This review consolidates existing knowledge on the diversity and epidemiology of phytoplasma-related mixed infections, with a particular emphasis on documented co-infections across various host plants and regions, especially in tropical and subtropical areas. Mixed infections affect disease severity, symptom expression, vector behavior, and pathogen dissemination, highlighting the limitations of pathogen-specific diagnostic and control strategies. The necessity for tools to detect multiple pathogens, enhanced understanding of pathogen–pathogen and host–pathogen interactions, and comprehensive surveillance systems is emphasized. Ultimately, breeding for resistance must consider the complexities of natural co-infections to ensure effective protection of crops. Addressing the challenges presented by phytoplasma-related mixed infections is crucial for developing resilient and sustainable plant health strategies in the face of increasing ecological and agricultural pressures. Full article