Agricultural Environment Research Articles

Survey of Fungal Diseases on Barley, Wheat, and Oats at Tillering to Stem Extension Stages in Southern Regions of Korea during 2020-2021

Cereal, including barley, wheat, and oats, is a major winter food crop in Korea. Despite recent changes in agricultural environments in response to climate change, fungal diseases that could affect cereal productivity remain poorly understood. In this study, we investigated the incidence of diseases in barley, wheat, and oats in the southern part of Korea. We collected fungal pathogens from seven locations where cereals were grown. In March-April of 2020 and 2021, a total of 92 fungal isolates were collected, mainly from the stem base or leaves of cereal crops during the tillering and stem extension stages of cereals in Korea. The collected isolates were identified based on morphological and molecular biological characteristics. The dominant species was <i>Ceratobasidium cereale</i> (42.4%), followed by <i>Pyrenophora teres</i> (21.7%), <i>P. avenae</i> (10.9%), <i>Alternaria alternata</i> (6.5%), and <i>Epicoccum tobaicum</i> (6.5%). In addition, <i>P. tritici-repentis</i> (3.3%), <i>Cladosporium</i> sp. (3.3%), <i>Fusarium</i> sp. (3.3%), and <i>Nigrospora</i> sp. (2.2%) were also collected as minority groups. Our results will provide information on fungal pathogens that occur during the growing season of cereals in Korea, particularly during the tillering and stem extension stages. In addition, the isolates collected from this study can serve as a valuable resource for conducting simulations on climate change, focusing on temperature and humidity.

Changing Agriculture Patterns and Environment Concerns

Over fifty-percent of India's population depends on agriculture for their living, as it plays a major role in the country's food security. Agriculture has been and is still the foundation of the Indian economy. From Post-Independence subsistence-oriented and feudal systems to modern technologically sophisticated and market-driven agriculture, the sector's history is shaped by government reforms, technological advances, and socio-economic shifts. In this context this paper examines the Post-Independence agrarian structure, the transformative Green Revolution which comes with a number of environment concerns ranging from increased green-house gas emission contributing to global warming and climate change accompanied by land degradation, soil and water pollution. The paper also critically assesses the implications of climate change on crop production, water resources, and rural health and livelihoods and outlines future directions for sustaining agricultural growth and ensuring food security.

Detection of Porcine Circovirus (PCV) Using CRISPR-Cas12a/13a Coupled with Isothermal Amplification.

The impact of porcine circovirus (PCV) on the worldwide pig industry is profound, leading to notable economic losses. Early and prompt identification of PCV is essential in managing and controlling this disease effectively. A range of detection techniques for PCV have been developed and primarily divided into two categories focusing on nucleic acid or serum antibody identification. The methodologies encompass conventional polymerase chain reaction (PCR), real-time fluorescence quantitative PCR (qPCR), fluorescence in situ hybridization (FISH), loop-mediated isothermal amplification (LAMP), immunofluorescence assay (IFA), immunohistochemistry (IHC), and enzyme-linked immunosorbent assay (ELISA). Despite their efficacy, these techniques are often impeded by the necessity for substantial investment in equipment, specialized knowledge, and intricate procedural steps, which complicate their application in real-time field detections. To surmount these challenges, a sensitive, rapid, and specific PCV detection method using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas12a/13a coupled with isothermal amplification, such as enzymatic recombinase amplification (ERA), recombinase polymerase amplification (RPA), and loop-mediated isothermal amplification (LAMP), has been developed. This novel method has undergone meticulous optimization for detecting PCV types 2, 3, and 4, boasting a remarkable sensitivity to identify a single copy per microliter. The specificity of this technique is exemplary, with no observable interaction with other porcine viruses such as PEDV, PRRSV, PRV, and CSFV. Its reliability has been validated with clinical samples, where it produced a perfect alignment with qPCR findings, showcasing a 100% coincidence rate. The elegance of merging CRISPR-Cas technology with isothermal amplification assays lies in its on-site testing without the need for expensive tools or trained personnel, rendering it exceptionally suitable for on-site applications, especially in resource-constrained swine farming environments. This review assesses and compares the process and characteristics inherent in the utilization of ERA/LAMP/RPA-CRISPR-Cas12a/Cas13a methodologies for the detection of PCV, providing critical insights into their practicality and effectiveness.

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

CHARACTERIZATION OF RUNOFF ON AGRICULTURAL SOILS IN THE BANDAMA BLANC WATER-SHED, NORTHERN IVORY COAST, USING THE SCS-CN METHOD WITH GEOGRAPHIC INFOR-MATION SYSTEM (GIS) TOOLS

This study focused on the characterization of runoff on agricultural soils in theBandama Blanc watershed in Badikaha, Northern Cote dIvoire. The objective was to highlight the influence of land use change on runoff. To achieve this, soil data and profiles were utilized, allowing for the analysis of the hydrological properties and characteristics of the soils in the study area using the SCS (U.S.D.A.) method. The study revealed a continuous increase in the proportions of built-up/bare soil and cultivated areas over three study periods. The respective proportions were 16%, 27%, and 48% for built-up/bare soil, and 11%, 17%, and 33% for cultivated areas in the years 1986, 2004, and 2023. Additionally, it showed that the soils in the watershed predominantly exhibit clayey texture. These findings, combined with curve numbers (CN), highlighted significant runoff occurring in the study area, with average CN values of 70.57, 57.14, and 66.67 for 1986, 2004, and 2023, respectively. It follows from these results that runoff is more pronounced in urban areas, bare soils, highly mechanized agricultural environments, and watercourses. This study demonstrated that GIS and remote sensing techniques enable large-scale determination of runoff using the SCS-CN method. Furthermore, it serves as a simple and cost-effective tool for modeling runoff.

Lure Monitoring for Mediterranean Fruit Fly Traps Using Air Quality Sensors.

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO2). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO2 sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments.

The Impact of Technological Innovations on Agricultural Productivity and Environmental Sustainability in China

Agricultural productivity in China is a fundamental driver of food security and economic growth. Yet, the sector faces profound challenges due to environmental degradation and climate change, which threaten sustainable agricultural practices. This research examines the effects of technological innovations on agricultural Total Factor Productivity and environmental sustainability in China from 2012 to 2022. The study seeks to understand how technological advancements, when considered alongside socioeconomic variables, impact agricultural output while balancing ecological integrity. Employing a comprehensive methodological framework, this research integrates fixed-effects, random-effects, and multilevel mixed-effects models to analyze crucial factors including rural education, technological capability, and environmental conservation initiatives. The study further utilizes structural equation modeling to evaluate both the direct and indirect effects of these determinants on productivity. The results demonstrate that technological innovations substantially enhance agricultural productivity, particularly in provinces with higher socioeconomic development. Additionally, sustainable farming practices and tailored policy interventions are identified as vital in addressing regional productivity imbalances. The research concludes by underscoring the necessity for the continued integration of environmental considerations and emerging technologies to ensure the sustainability of agricultural growth in the long term.

Soil Nutrient Analysis and Automatic Monitoring in Precision Agriculture

ABSTRACT The higher productivity from soil can be achieved through Wireless Sensor Networks (WSNs) technology; for this reason, the implementation of WSNs in precision agriculture is increasing day by day. Among the different technologies for crop monitoring, WSNs are recognized as a powerful option for collecting and processing data in the agricultural domain, with low cost and low energy consumption. The ultimate aim of this paper is to predict the precise values of soil nutrients nitrogen, phosphorus, and potassium (NPK) using a machine learning (ML)-based mathematical model formulation. The proposed system will use Global System for Mobile Communications (GSM) technology to automatically monitor soil parameters. It transfers soil data over the mobile network using a GSM modem. In the hardware part, sensor node units were developed to fetch and load data through the energy-efficient GSM, utilizing a bi-directional multi-level text messaging option to improve the alarm system’s efficiency. The system contains sensors such as potential of hydrogen and humidity (PH), which are used to monitor soil data collected from the sensors by the microcontroller. Additionally, as a novel contribution, machine learning based on regression models is applied to the NPK data. A novel data interpolation approach is proposed for data oversampling. The R2 values are compared, achieving nearly 99.5% accuracy in all cases. This paper focuses on various challenges and scalability issues encountered in research and discusses different results obtained by monitoring various soil parameters in a real-time agricultural environment.

Influence of Height on the Abundance and Richness of Green Lacewings (Neuroptera: Chrysopidae) in an Anthropically Modified Habitat.

Investigation of the diversity of lacewings (Neuroptera: Chrysopidae) is crucial in agricultural systems, as these insects are important predators of many pest species. Recent studies have shown that height influences the abundance and richness of insects. Information on these factors is scarce for species of Chrysopidae at different heights in agricultural and forest environments. This study aimed to evaluate the influence of height on the abundance and richness of green lacewings in an anthropically modified environment. Multilure traps baited with putrescine and ammonium acetate were placed in four areas of the "Luiz de Queiroz" Campus (Piracicaba, São Paulo State, Brazil) at two heights (2 and 10m) from April 2019 to March 2020. Species captured in traps hung at 2 and 10m above the ground surface were studied with the faunistic analysis ANAFAU, using ecological parameters and diversity indices. The similarity between the species was measured using the Morisita-Horn coefficient. Thirteen species of Chrysopidae were captured, and more species and specimens of green lacewings were collected with the traps at 2m than at 10m, that is, height influenced the abundance and richness of the lacewing species. Leucochrysa (Nodita) cruentata (Schneider) was predominant in most areas studied. The highest diversity and abundance of green lacewings was observed on Areão farm in trap at 2m height. However, Ceraeochrysa claveri (Navás) and Ce. everes (Banks) showed a more similar ecological pattern. Multilure traps baited with putrescine and ammonium acetate were effective in sampling green lacewings, mainly at 2m height.

The joint toxicity effect of glyphosate and cadmium in a concentration-dependent manner on nematode Caenorhabditis elegans

The co-occurrence of glyphosate (GPS), a commonly used organophosphorus herbicide, and cadmium (Cd), a neurotoxic metal, in agricultural environments prompts concerns about their combined toxic effects on ecosystems. This study explores the combined effects of GPS and Cd on the model organism Caenorhabditis elegans (C. elegans), to understand their cumulative effects in organismal living environments. We investigated the interaction between GPS and Cd over 24 hours using a comprehensive approach that included a variety of toxicity endpoints as well as the novel Automated Recognition and Statistics Tool (NCLE) for body bend measurement. Our data show a concentration-dependent interplay in which antagonistic effects at lower concentrations reduce phenotypic damage while synergistic effects emerge at higher concentrations, particularly at GPS's LC50. Transcriptome analysis under antagonistic conditions revealed significant downregulation of Cd toxicity-related genes and identified Y22D7AL.16, which has a C2H2-type zinc finger domain, as a novel gene involved in metal stress response, implying an alternative Cd-resilience mechanism. The expression profile of this gene shows that it plays a larger role in both development and metal stress adaption. These findings highlight the complexities of compound pollutant interactions, emphasizing the importance of including such dynamics in environmental risk assessments and control techniques.

Exploring the genetic variability, virulence factors, and antibiotic resistance of Listeria monocytogenes from fresh produce, ready-to-eat hummus, and food-processing environments.

Listeria monocytogenes is ubiquitous in nature and persistent in food-processing facilities, farms, retail stores, and home and restaurant kitchens. Current research suggests ready-to-eat (RTE) products (including RTE hummus and fresh produce) to be of increasing interest and concern. These foods are typically stored at refrigeration temperatures suited to the survival of L. monocytogenes and are consumed without further processing. Since L. monocytogenes is ubiquitous in agricultural environments, the cultivation of fresh produce predisposes it to contamination. The contamination of RTE foods originates either from raw ingredients or, more commonly, from cross-contamination within food-processing facilities. Research on the food-processing environment has been recommended to reduce the incidence of L. monocytogenes in foods. The consumption of contaminated foods by immunocompromised individuals causes invasive listeriosis, with a 20% to 30% fatality rate despite treatment. The emergence of antibiotic-resistant strains has reduced the effectiveness of modern medicine and may increase morbidity and mortality. Without epidemiological surveillance and identifying trends in disease determinants, no action can be taken to improve food safety and mitigate the risk of such outbreaks.

A Comprehensive Review of Machine Learning Approaches in Livestock Health Monitoring

Livestock health monitoring has emerged as a crucial area in agricultural technology, where Machine Learning (ML) approaches offer promising solutions for disease detection, heat stress management, and behavioral anomaly identification. This review explores the latest advancements in applying machine learning models to livestock health monitoring, focusing on methods such as deep learning, IoT integration, and multimodal frameworks. Techniques like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and hybrid models have significantly improved early detection of communicable and non-communicable diseases. Additionally, AI-based systems monitoring heat stress and behavioral changes have enhanced overall livestock management, reducing economic losses and improving animal welfare. This review synthesizes current trends and challenges in deploying ML models in real-world farming environments, particularly integrating IoT devices to collect and analyze health data. Moreover, it highlights future directions in improving the accuracy and scalability of ML models, including optimizing data collection and leveraging real-time analytics. This review aims to guide future developments in AI-driven livestock health monitoring systems by providing a comprehensive overview of current research.

Automated body measurement of beef cattle based on keypoint detection and local point cloud clustering

Abstract Body size parameters of beef cattle are crucial for assessing growth status and breeding value. In actual farming environments, the various postures of beef cattle and complex backgrounds can affect the accuracy and stability of non-contact body measurement methods. Therefore, this paper proposes a novel method called the cattle body measurement method (CBMM), which combines keypoint detection with local point cloud clustering. First, a keypoint detection model based on YOLOv8-SimBiFPN is constructed. This model enhances the feature extraction and fusion capabilities of YOLOv8-pose by introducing SimAM and BiFPN into the backbone and neck networks, respectively, and realizes 2D keypoint detection for beef cattle in various postures. Second, a 3D keypoint-locating algorithm based on Density-based spatial clustering of applications with noise (DBSCAN) is proposed. This algorithm utilizes 2D keypoints, depth maps and camera parameters to generate local point clouds, which are then clustered using DBSCAN to segment cattle body point clouds, thereby relocating the 3D keypoints based on their positional features. Finally, body size parameters are calculated based on the 3D keypoints and distance formulae. In our experiment, the mean average precision (mAP@0.5) of YOLOv8-SimBiFPN reached 99.1% on an Angus beef cattle keypoint detection dataset. The mean absolute percentage errors for measuring beef cattle withers height, hip height, body depth, body length, and oblique body length using the CBMM were 4.37%, 4.96%, 6.47%, 4.84%, and 4.14%, respectively. In summary, our method can achieve non-contact body measurement for beef cattle in a free-moving state with high accuracy and stability.

Soil Sample Analysis of Bacillus anthracis Contaminated Animal Burial Sites.

Environmental contamination with Bacillus anthracis spores poses clear threats to livestock that play key roles in the economies of pastoral communities. Regular monitoring of contaminated sites is particularly important in anthrax-endemic parts of the world, such as Kars province in eastern Türkiye, where the Veterinary Microbiology Department of Kafkas University has conducted an anthrax surveillance programme for over 30 years. We reviewed the microbiological results of 232 soil samples collected during 2009-2023, from sites known to be contaminated with B. anthracis spores following burial or butchering of infected animal carcasses. Twenty-five contaminated sites in 16 villages were studied. Samples were taken from a total of 61 different positions within these sites and viable spores were detected in 136 (58.6%) of the samples examined. Of the 96 samples from which spores were not recovered, subsequent samples from the same positions proved positive on 21 occasions. Using a standardised sampling plan, it was discovered that samples taken 1-2 m on a downward slope from the centre-point of contamination had higher (p < 0.001) spore concentrations than those taken from other positions. Although spore concentrations at some sampling positions varied over time, the overall values remained stable. This finding contrasts with observations in other parts of the world where spore concentrations tend to decline with time and may reflect regional differences in soil composition that permit more prolonged spore persistence. Concentrations of >100 spores/g soil were found in 10 (66.7%) of the 15 samples taken 10-13 years following a contamination event. These results demonstrate the longevity of viable anthrax spores in the soil of agricultural environments following decomposition of infected animal carcasses, and therefore the need for prolonged bacteriological monitoring of contaminated sites. Furthermore, they underline the importance of appropriate decontamination, as burial on its own does not eliminate all spores.

Ecological characteristics of tall fescue and spatially organized communities: Their contribution to mitigating cadmium damage

The threat of cadmium (Cd) stress to agricultural soil environments, as well as their productivity attracting growing global interest. Tall fescue (Festuca arundinacea Schreb.) is a strong candidate for the remediation of heavy metals in soil. However, the joint analysis of Cd tolerance, physiological responses, and multifaceted plant microbiomes in tall fescue fields has not been extensively researched. Therefore, this study employed microbial sequencing (i.e., 16S and ITS sequencing) to investigate the differences in microbial community structure among various plant compartments of Cd-resistant tall fescue (cv. ‘Arid3′) and Cd-sensitive tall fescue (cv. ‘Barrington’). Furthermore, we examined the mechanism of resistance to Cd by introducing three different bacteria and a fungus that were isolated from the ‘Arid3′ rhizosheath soil. It highlighted the potential application of enriched taxa such as Delftia, Novosphingobium, Cupriavidus and Torula in enhancing the activity of antioxidant defense systems, increasing the production of osmotic regulatory substances, and stimulating the expression of Cd-resistance genes. This ultimately promoted plant growth and enhanced phytoremediation efficiency. This study shed light on the response mechanism of the tall fescue microbiome to Cd stress and underscored the potential of tall fescue-microbe co-culture in the remediation of heavy metal-contaminated areas.

Wet-dry or freeze-thaw alternation can regulate the impacts of farmland plastic pollution on soil bacterial communities and functions

The persistence of farmland plastic pollution has raised significant concerns regarding its potential long-term impacts on soil health in the context of global climate change. However, there are still gaps in the understanding of the impacts of plastic residues on soil microbial communities and functions in agricultural environments under unstable and extreme climatic conditions. In this study, the effects of plastic residues (two types and three shapes) on farmland soil bacterial communities and functions across varying environmental conditions were investigated through microscopic experiments. The results revealed that plastic residues subjected to wet-dry or freeze-thaw alternations exhibited greater degradation compared to those under natural conditions. The effects of plastic residue types and shapes on soil bacterial diversity and function were regulated by environmental factors. The plastic residues significantly reduced the stability of the bacterial network under natural condition (P < 0.05), whereas the opposite phenomenon was observed under wet-dry or freeze-thaw alternating conditions. Compared to under natural condition, lower numbers of bacterial functional pathways exhibiting significant differences due to plastic residues were observed under wet-dry or freeze-thaw alternating conditions. Significant associations were observed between soil bacterial communities and functions and various soil physicochemical properties under natural conditions (P < 0.05), and most of these associations were attenuated in the wet-dry or freeze-thaw alternations. This study demonstrated the potential impacts of plastic pollution on farmland soil microbiomes, which could be modulated by both residue characteristics and climatic conditions. Specifically, extreme environments could mitigate plastic-pollution-driven influences on soil microbiomes.

Comparative study of caná – a fermented sugarcane beverage produced from culms with and without bark

Cachaça is a Brazilian beverage produced by distilling fermented must from sugarcane juice, with an alcohol content ranging from 38% and 48% v/v. Currently, thousands of “Alembics” (small-scale destilleries located in agricultural environments) produce cachaça at a high standard of quality with both national and international recognition. These producers possess the infrastructure, knowledge, and expertise required to introduce Caná (a fermented sugarcane beverage) into their production line. According to Brazilian legislation, the alcohol concentration for this beverage is defined to be within the range of 4% to 14%. Being only fermented, Caná retains the phenolic compounds from the juice, which have a high nutritional value. Furthermore, following the example of the European Community, there are prospects for Brazil to implement lower tax rates for beverages produced in small-scale factories with a low alcohol content. This context means that Caná is a significant option for enhancing the competitiveness of Alembics in both the Brazilian and international markets. In this work, a comparison was made between Caná produced from juice obtained from intact stalks and the core (after removing the woody wall). The sensory results were markedly different, and there is still a need for some operational adjustments. Both beverages were positively evaluated, which confirms the prospect of a wide range of colors and flavors for Caná resulting from the use of different varieties of sugarcane, and there exists the possibility of numerous adjustments in operational procedures.

The Contribution of Dairy Bedding and Silage to the Dissemination of Genes Coding for Antimicrobial Resistance: A Narrative Review.

Antimicrobial resistance (AMR) is a concern in the dairy industry. Recent studies have indicated that bedding serves as a reservoir for antimicrobial-resistant bacteria and antimicrobial-resistance genes (ARGs), while silage has been proposed as another possible source. The impact of AMR in dairy farming can be significant, resulting in decreased productivity and economic losses for farmers. Several studies have highlighted the safety implications of AMR bacteria and genes in bedding and silage, emphasizing the need for further research on how housing, bedding, and silage management affect AMR in farm environments. Exposure to sub-lethal concentrations of antibiotics, such as those from contaminated bedding and silage, can prompt bacteria to develop resistance mechanisms. Thus, even if antimicrobial usage is diminished, ARGs may be maintained in the dairy farm environment. By implementing proactive measures to tackle AMR in dairy farming, we can take steps to preserve the health and productivity of dairy cattle while also protecting public health. This involves addressing the prudent use of antibiotics during production and promoting animal welfare, hygiene, and management practices in bedding and farm environments to minimize the risk of AMR development and spread. This narrative review compiles the growing research, positioning the contribution of bedding and silage to the prevalence and dissemination of AMR, which can elicit insights for researchers and policymakers.

Sequence Types and Antimicrobial Resistance Profiles of Salmonella Typhimurium in the Food Chain in Singapore.

Salmonella remains a significant foodborne pathogen globally with S. Typhimurium presenting as a frequently occurring serovar. This study aimed to characterize 67 S. Typhimurium isolates from humans, food, farms, and slaughterhouses collected in Singapore from 2016 to 2017. Using whole-genome sequencing analysis, the isolates were found to belong to either ST19 (n = 33) or ST36 (n = 34). ST36 predominated in human intestinal and chicken isolates, while human extra-intestinal and non-chicken food isolates belonged to ST19. Plasmids were predicted in 88.1% (n = 59) of the isolates with the most common incompatibility group profiles being IncFIB(S), IncFII(S) and IncQ1. IncFIB(S) (adjusted p-value < 0.05) and IncFII(S) (adjusted p-value < 0.05) were significantly more prevalent in ST19 isolates, while Col156 (adjusted p-value < 0.05) was more significantly found in ST36 isolates. ST36 isolates exhibited higher resistance to multiple antibiotic classes such as penicillins, phenicols, folate pathway inhibitors, aminoglycosides, β-lactam/β-lactamase inhibitor combinations, tetracyclines, and fluoroquinolones. Phylogenetics analysis suggested potential shared routes of transmission among human, chicken, farm and slaughterhouse environments. Taken together, this study offers a cross-sectional epidemiological insight into the genomic epidemiology and antimicrobial landscape of S. Typhimurium isolates in Singapore, informing strategies for future public health and food safety surveillance.

Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop. IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. The environmental factor that is active at all times and has an effect on a crop from its cultivation to harvest. With the aid of an IoT, we will monitor the following factors: temperature, humidity, and leaf wetness, all of which have an impact on the overall quality and lifespan of grapes. A Self-created database of weather parameter using sensors is introduced in this article. It consists of 5 categories with a total of 10,000 records. Here, experiment has been carried out using our dataset to predict grape diseases on various machines learning algorithm. The system receives overall accuracy of 98.25 % for Powdery Mildew, 98.85 % for Downy Mildew and 93.95 % for Bacterial Leaf Spot.