ABSTRACT The diagnosis of mosquito-borne viral infectious diseases can be challenging, in part due to the complexity of antibody cross-reactivity between many of these viruses. This is further complicated by the unpredictable nature of climatic variability affecting disease transmission, exotic virus incursion, and the potential emergence of new strains of viruses with increased virulence. A thorough understanding of virus biology, locally relevant transmission patterns, and principles of diagnostic tests is required for the investigation of suspected clinical cases. This review provides guidance for veterinarians, researchers, and policy-makers on the diagnosis and management of alphavirus and orthoflavivirus infections in animals with a One Health perspective, including interpretation of laboratory results. Biosecurity and biosafety considerations and the zoonotic potential of mosquito-borne infections are also discussed.
The United States designates Food and Agriculture as one of sixteen critical infrastructure sectors, yet no mandatory cybersecurity requirements exist for agricultural operations and no formal threat model has been published for Controlled Environment Agriculture (CEA) systems. This paper presents the first comprehensive threat model for IoT-enabled CEA, applying STRIDE analysis, MITRE ATT&CK for ICS mapping, and IEC 62443 zone-and-conduit decomposition to a production platform deployed across 30+ commercial facilities in 8 U.S. climate zones. We enumerate 123 unique threats across 25 data-flow-diagram elements spanning 15 communication protocols, 10 of which operate with zero authentication or encryption by design. We identify five novel attack classes unique to AI-driven CEA: stealth destabilization of neural-network-tuned PID controllers, baseline drift poisoning of anomaly detectors, cross-facility propagation via federated transfer learning, adversarial agronomic schedules that exploit crop biology rather than computational models, and reward poisoning of reinforcement-learning energy optimizers. Physical impact analysis quantifies crop loss timelines from minutes (aeroponics) to days, including worker safety hazards from CO2 injection manipulation. A survey of 10 commercial CEA vendors reveals only one CVE ever issued, zero bug bounty programs, and zero IEC 62443 certifications. We propose a defense-in-depth countermeasure framework and recommend Security Level 2 as a minimum baseline.
The current inventory practice in bareroot forest nurseries relies on manually counting tree seedlings in randomly sampled plots to estimate the stock for each seed lot. This method is labor-intensive, time-consuming, and susceptible to human error. Recent advances in deep learning-based object detection and efficient tracking algorithms have enabled automated object counting in video data across various domains, including crop seedling counting in agriculture. This study investigates the effects of viewing angle (VA) and field of view (FoV) on detection, tracking, and counting early-stage pine seedlings in nadir-view videos using a detect-and-track approach. We evaluated the performance of YOLOv8–10 models in conjunction with three multi-object tracking (MOT) algorithms (SORT, ByteTrack, and BoT-SORT) on a custom MOT dataset comprising an average of 153 seedlings per frame and totaling 166,440 seedlings. Detection results and statistical tests showed that increasing horizontal VA reduces the intersection over union (IoU) of seedling detections, primarily due to the perspective differences introduced by oblique viewing angles. MOT evaluations further demonstrated that BoT-SORT consistently delivered high counting accuracy when the vertical FoV encompassed at least the entire seedling. In contrast, ByteTrack and SORT exhibited significantly lower performance, producing reasonable counting accuracy only when the vertical FoV was sufficiently large. The superior performance of BoT-SORT is attributed to its camera motion compensation, which effectively reduces identity switches and tracking failures in scenes involving stationary yet overlapping seedlings. Notably, BoT-SORT achieved 100 % counting accuracy under a 20° horizontal VA across YOLO model sizes. Furthermore, larger YOLO models showed greater robustness to increases in horizontal VA. These findings provide valuable guidance for optimizing camera configurations and model selection towards the development of a real-time inventory systems for precision forest nursery management.
Abstract Mushroom cultivation represents an auspicious avenue to address poverty and food insecurity in land-scarce countries like Rwanda. However, nearly two decades after it was introduced in Rwanda’s agricultural system, little is known about the farm-level practices, benefits, as well as the challenges facing the mushroom industry in Rwanda. Understanding these aspects is yet paramount to mark progress and identify future policy objectives. This study addresses the knowledge gap by examining farm-level practices, assessing the perceived benefits and challenges; and exploring how Rwanda’s mushroom value chain is organised, based on the integrated value chain (VC), new institutional economics (NIE) and structure-conduct-performance (SCP) framework. We conducted non-participant observation and semi-structured interviews with mushroom farmers and official representatives nationwide; and performed content analysis using MaxQDA 24.3.0 to analyse the data. Our findings reveal an industry dominated by uncoordinated smallholder farmers, influenced by government-linked actors. Using locally available crop residues, imported cotton husks, and the seedling spawn sourced from a single government-recognised agency, farmers produce oyster mushrooms which they sell fresh to consumers and traders in major markets in Kigali, using commercial public buses as the main mode of delivery. In addition to the perceived role of addressing malnutrition, mushroom farming provides meaningful employment, rapid incomes and is a reliable source of livelihood. The major challenges include the absence of post-harvest handling technologies, limited farmers’ knowledge, lack of spawn, and limited consumer awareness. These findings suggest that value chain development efforts should focus on improving farmers' knowledge, raising consumer awareness, diversifying quality spawn sources, and supporting farmers with the necessary equipment.
Nutrition. Foods and food supply, Agricultural industries
Ígor Boninsenha, Daran R. Rudnick, Everardo C. Mantovani
et al.
Efficient agricultural water management ensures crop productivity and sustainability amidst climate change and water scarcity. This study integrates remote sensing and deep learning to advance irrigation uniformity monitoring by identifying sources of non-uniformity. Sentinel-2 satellite imagery from 2021–2023 was processed to generate 159,088 NDVI images from 1382 center pivot irrigation systems in Mato Grosso, Brazil. These images were classified into nine categories: vegetated, not vegetated, emitters, mechanical problems, low pressure, management zones, operational, partial crop, and clouds. Artificial images mimicking these patterns pre-trained a DenseNet121 convolutional neural network (CNN), addressing the challenge of limited labeled training data. Fine-tuning with six subsets of satellite data (2000–20,000 images) enhanced performance, achieving a Hamming accuracy of 99 % and an Exact Match accuracy of 91 %. Class-specific metrics demonstrated high precision, recall, and F1 scores for most patterns, though underrepresented classes, like mechanical issues, showed lower performance. The methodology was applied to 80 pivots in Mato Grosso (January–October 2024) using 2752 images, integrating classification results with the Satellite-Derived Christiansen Uniformity Coefficient (SDCUC). Among the pivots, 45 showed high uniformity (>90 % SDCUC), with 10 exhibiting irrigation-related issues, and 28 facing non-irrigation challenges. Another 32 pivots had acceptable uniformity (80–90 %), with 9 linked to irrigation problems and 25 to non-irrigation issues. Finally, 3 pivots had low uniformity (<80 %), with all issues related to non-irrigation factors like partial crop coverage. This scalable approach offers actionable insights for addressing non-uniformity, improving irrigation efficiency, and supporting precision agriculture, large-scale water management, and policymaking.
Kiwifruit harvesting is labor-intensive, and social issues like an aging population and a declining agricultural workforce have significantly increased costs, presenting unprecedented challenges to the industry. Automatic harvesting systems utilizing multi-sensor fusion, AI, and automation technologies show great potential for replacing manual labor in kiwi harvesting. This paper reviews over 140 research articles related to kiwi fruit harvesting robots, summarizing existing progress in two key areas: target fruit recognition and positioning systems, and fruit picking and collection systems. We compare the pros and cons of various methods, including traditional image recognition and deep learning, active and passive localization techniques, diverse end-effector design structure and driving mechanisms, robotic arm path planning, and harvesting systems. The results show that challenges remain in the commercialization of kiwi harvesting robots. The absence of a unified evaluation standard for robot performance makes the latest research achievements hard to be inherited, leading to slow advancements. Current algorithms are often not lightweight enough for low-cost embedded systems. Additionally, the reliance on manual labeling of dense targets and the accumulation of system error compromise the robustness of target recognition and spatial positioning in open environments. The existing studies tend to focus on local improvements rather than the entire harvesting system. So addressing these issues should be a priority for future research. This paper can provide a reference for researchers and assist industry professionals in understanding the trends in harvesting robot development.
The growing scale of modern farms has increased the need for efficient and adaptive multi-agent coverage strategies for pest, weed, and disease management. Traditional methods such as manual inspection and blanket pesticide spraying often lead to excessive chemical use, resource waste, and environmental impact. While unmanned aerial vehicles (UAVs) offer a promising platform for precision agriculture through targeted spraying and improved operational efficiency, existing UAV-based approaches remain limited by battery life, payload capacity, and scalability, especially in large fields where single-UAV or uniformly distributed spraying is insufficient. Although multi-UAV coordination has been explored, many current frameworks still assume uniform spraying and do not account for infestation severity, UAV dynamics, non-uniform resource allocation, or energy-efficient coordination. To address these limitations, this paper proposes a Density-Driven Optimal Control (D2OC) framework that integrates Optimal Transport (OT) theory with multi-UAV coverage control for large-scale agricultural spraying. The method supports non-uniform, priority-aware resource allocation based on infestation intensity, reducing unnecessary chemical application. UAVs are modeled as a linear time-varying (LTV) system to capture variations in mass and inertia during spraying missions. The D2OC control law, derived using Lagrangian mechanics, enables efficient coordination, balanced workload distribution, and improved mission duration. Simulation results demonstrate that the proposed approach outperforms uniform spraying and Spectral Multiscale Coverage (SMC) in coverage efficiency, chemical reduction, and operational sustainability, providing a scalable solution for smart agriculture.
Direct economic impacts of flooding are essential for flood mitigation policies, and are based upon multiple tools, among which 2D hydrodynamic models. These models are dependent on multiple parameters and data, such as topography, and can yield considerably different results depending on their values, ultimately changing the resultant damage estimation. To help in understanding some of the underlying drivers of this variation, this conference paper compares damage estimations issued by two different hydrodynamic models of the Garonne River, near Marmande, in France. The influence of a topo-bathymetry projection method, which aims at reducing some interpolation incoherencies, was also studied. The general conclusion is that these two factors, i.e., a change in the model parameters and the topo-bathymetry projection method, do matter but their influence on the total cost estimation is highly dependent on the description of a few high-vulnerability agricultural fields. Indeed, the latter often account for the majority of the total damage even if occupying a fraction of the total area. This paper then advocates for a more vulnerability-based modelling approach, with a focus on the few high-vulnerability areas.
Sadia Afrin Rimi, Md. Jalal Uddin Chowdhury, Rifat Abdullah
et al.
The number of people living in this agricultural nation of ours, which is surrounded by lush greenery, is growing on a daily basis. As a result of this, the level of arable land is decreasing, as well as residential houses and industrial factories. The food crisis is becoming the main threat for us in the upcoming days. Because on the one hand, the population is increasing, and on the other hand, the amount of food crop production is decreasing due to the attack of diseases. Rice is one of the most significant cultivated crops since it provides food for more than half of the world's population. Bangladesh is dependent on rice (Oryza sativa) as a vital crop for its agriculture, but it faces a significant problem as a result of the ongoing decline in rice yield brought on by common diseases. Early disease detection is the main difficulty in rice crop cultivation. In this paper, we proposed our own dataset, which was collected from the Bangladesh field, and also applied deep learning and transfer learning models for the evaluation of the datasets. We elaborately explain our dataset and also give direction for further research work to serve society using this dataset. We applied a light CNN model and pre-trained InceptionNet-V2, EfficientNet-V2, and MobileNet-V2 models, which achieved 91.5% performance for the EfficientNet-V2 model of this work. The results obtained assaulted other models and even exceeded approaches that are considered to be part of the state of the art. It has been demonstrated by this study that it is possible to precisely and effectively identify diseases that affect rice leaves using this unbiased datasets. After analysis of the performance of different models, the proposed datasets are significant for the society for research work to provide solutions for decreasing rice leaf disease.
Trap cropping is a pest management strategy where a grower plants an attractive "trap crop" alongside the primary crop to divert pests away from it. We propose a simple framework for optimizing the proportion of a grower's field or greenhouse allocated to a main crop and a trap crop to maximize agricultural yield. We implement this framework using a model of pest movement governed by trap crop attractiveness, the potential yield threatened by pests, and functional relationships between yield loss and pest density drawn from the literature. Focusing on a simple case in which pests move freely across the field and are attracted to traps solely by their relative attractiveness, we find that allocating 5-20 percent of the landscape to trap plants is typically required to maximize yield and achieve effective pest control in the absence of pesticides. For highly attractive trap plants, growers can devote less space because they are more effective; less attractive plants are ineffective even in large numbers. Intermediate attractiveness warrants the greatest investment in trap cropping. Our framework offers a transparent and tractable approach for exploring trade-offs in pest management and can be extended to incorporate more complex pest behaviors, crop spatial configurations, and economic considerations.
Biodiversity-associated ecosystem services such as pollination and biocontrol may be severely affected by emerging nano/micro-plastics (NMP) pollution. We synthesized the little-explored effects of NMP on pollinators and biocontrol agents on the organismal, farm and landscape scale. For instance ingested NMP trigger organismal changes from gene expression, organ damage to behavior modifications. At the farm and landscape level, NMP will likely amplify synergistic effects with other threats such as pathogens and antibiotics, and may alter landscape properties such as floral resource distributions in high NMP concentration areas, what we call NMP islands. It is essential to understand the functional exposure pathways of NMP on pollinators and biocontrol agents to comprehensively evaluate the risks for agricultural ecosystems and global food security.
Artificial Intelligence (AI) is fundamentally reshaping various industries by enhancing decision-making processes, optimizing operations, and unlocking new opportunities for innovation. This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail. Each section delves into the specific challenges faced by these industries, the AI technologies employed to address them, and the measurable impact on business outcomes and societal welfare. We also discuss the implications of AI integration, including ethical considerations, the future trajectory of AI development, and its potential to drive economic growth while posing challenges that need to be managed responsibly.
Javid Karamianpour, Hossein Arfaeinia, Dariush Ranjbar Vakilabadi
et al.
In this research, a total of 51 road dust samples were collected from three districts (Asaluyeh, Bushehr, and Goshoui) in the south of Iran from April to June 2022 and analyzed for the concentration of 7 phthalic acid esters (PAEs) compounds. Asaluyeh was considered as an industrial area (near gas and petrochemical industries), Bushehr as an urban area, and Goshoui as a rural area (far from pollution sources). The PAEs concentration of the street dust samples was determined using a mass detection gas chromatography (GC/MS). The mean ± SD levels of ƩPAEs in samples from industrial, urban, and rural sources were 56.9 ± 11.5, 18.3 ± 9.64, and 5.68 ± 1.85 μg/g, respectively. The mean concentration levels of ƩPAEs was significantly (P < 0.05) higher in samples from the industrial area than urban and rural areas. The mean levels of di(2-Ethylhexyl) phthalate (DEHP) in industrial, urban, and rural areas were 20.3 ± 8.76, 4.59 ± 1.71, and 2.35 ± 0.98 μg/g, respectively. The results of the PCA analysis indicate that the likely major sources of PAEs in the road dust in the studied areas are the application of various plasticizers in industry, solvents, chemical fertilizers, waste disposal, wastewater (e.g., agricultural, domestic, and industrial), and the use of plastic films and plastic-based irrigation pipes in greenhouses. As well as, it was found that the non-cancer risk of exposure to dust-bound PAEs was higher for children than for adults. These values were <1 for both age groups (children and adults) and the exposure of inhabitants to PAEs in road dust did not pose a notable non-cancer risk. The cancer risk from exposure to DEHP in road dust was below the standard range of 10−6 in all three areas. Further studies that consider different routes of exposure to these contaminants are needed for an accurate risk assessment. Moreover, since higher PAEs level was found in industrial area, decision-makers should adopt strict strategies to control the discharging of pollution from industries to the environment and human societies.
Climate change affects the security of the global water supply. However, only a few studies on the effects of drought on water quality in Asia have been published. In 2021, Taiwan faced its most severe water crisis in the past 56 years. The Te-Chi reservoir, situated at an elevation exceeding 1420 m, stands out as significantly affected by hydrologic variability. Overcoming geographical challenges, we captured an exceptional climatic period to sample and analyze water quality. Subsequently, we investigated multivariate long-term water quality data incorporating climate and hydrological information to evaluate the effect of climate change on the reservoir. The results showed the annual average temperature had risen by about 1.4 °C while total annual rainfall declined over the past 18 years. 30-year water quality data from 1993 to 2022 was analyzed to examined the long-term water quality change. There was an observed upward trend in the electrical conductivity of the reservoir water. The concentration of dissolved oxygen showed a decreasing trend, which may be attributed to the increase of temperature due to climate change. Besides, as the water level dropped during the drought, we found that the concentration of total phosphorus, total nitrogen and electrical conductivity in Te-Chi reservoir tended to increase, while the transparency of the water decreased. Consequently, we further explored the potential pollution sources during extreme drought by multivariate statistical methods, such as Pearson’s correlations, principal component analysis, and factor analysis. We found that four varimax rotated principal components collectively explain 82% of the variation in water quality data of the Te-Chi reservoir with the reservoir water level, signifying their representativeness in interpreting the monitoring data. We also assessed the contribution of each factor to specific water parameters, which can aid in better management of water resources during such challenging conditions. In conclusion, this study provides evidence and raises awareness on the effect of climate change by long-term water quality data with multiple parameters. The water quality components in the reservoir are affected by extreme weather, which is predicted to occur more frequently in the future and thus could affect energy scarcity, public health and food safety for sustainable reservoir operation. The study underscores adaptive management, mitigation strategies and the future research direction.
This research aims to ascertain the impact of CMC and kecombrang fruit juice addition on crystal guava Velva's organoleptic and physicochemical properties and the optimal treatment regimen to yield velva with the best properties. The two-factor factorial pattern utilized in this study's fully randomized design (CRD) has three repetitions. Factor I include 5%, 10%, and 15% of kecombrang fruit juice (v/v). Factor II, namely the addition of 0.5%, 0.75%, and 1% CMC. The conclusion in this study was 15% kecombrang fruit juice and 1% CMC concentration, which produced crystal guava velva with an antioxidant activity value of 41.543%; crude fiber content 6.270%; melting time 17.37 minutes/20gr; viscosity 4203 mPas; overrun 22.03 %; total solids 39.06 %; and the hedonic organoleptic test includes color 3.63; aroma 3.10; texture 3.73 and taste 3.27. The conclusion of this study shows that velva guava crystal has met the quality standards of velva.
Arti Kumbhar, Amruta Chougule, Priya Lokhande
et al.
Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely identifying faults by extracting intricate details from product photographs, utilizing RNNs to detect evolving errors and generating synthetic defect data to bolster the model's robustness and adaptability across various defect scenarios. The project leverages a deep learning framework to automate real-time flaw detection in the manufacturing process. It harnesses extensive datasets of annotated images to discern complex defect patterns. This integrated system seamlessly fits into production workflows, thereby boosting efficiency and elevating product quality. As a result, it reduces waste and operational costs, ultimately enhancing market competitiveness.
Catfish shredded has become one of the famous foods for many people due to it has a delicious taste and long-life storage of approximately 6-12 months in good storage condition. However, catfish shredded can still be contaminated with pathogenic bacteria that can be causing a healthy problem. Several bacteria that commonly appear in the shredded product are Salmonella, Staphylococcus aureus, and Escherichia coli thus its product need for assessing microbial contamination to ensure product safeties that will be supplied in the community. The microbial contamination assessment method in this research using Indonesian national standardization 2332.1:2015 for Escherichia coli assessment, 2332.2: 2006 for Salmonella assessment, 2332.3:2015 for total plate count assessment, and 2332.9:2015 for Staphylococcus aureus assessment. The catfish shredded sample that is used in this research was obtained from the Regional Technical Implementation Unit Application of Fishery Product Quality, Lampung. The result of the total plate count assessment on this research reported that all of the samples have a total amount of microbes lower than the maximum limit for the fish shredded product. Furthermore, the result also shows that all of the samples are negative from Salmonella, S. aureus, and Escherichia coli. According to Indonesian National Standardization number 7690.1:2013 about the standard of shredded products, all of the samples are appropriate and categorized as a safety product to consume based-on microbial contamination aspect.
Mehdi Rahimian, Hossein Zareei, Mandana , Masoudirad,
Planning for the development of low-income villages, on the other hand, technology simplicity, the need for less capital, a shorter period of return on investment and other benefits of small industries, the scale of the agricultural sector, the need to launch such industries has made it more visible than before. However, the path of launching these industries is not always simple and faces various obstacles. Hence, the purpose of this study was to identify and determine the barriers to the establishment of small-scale industries in the agricultural sector. To identify the barriers, 20 semi-structured interviews were conducted with provincial experts. Analysis of data collected from interviews led to the identification of 41 concepts in the form of 7 macro-categories of barriers. Then, the two-stage Delphi-fuzzy technique was used to reach a group agreement between the experts and to rank the inhibitory factors. According to the results of the Delphi-fuzzy technique, barriers to establishment of the small-scale industries in the agricultural sector in Kakavand district of Delfan city were classified into 5 categories. Financial-credit barriers with an average of 0.442, were the first priority barriers. Also, administrative-political barriers with an average of 0.426, lack of trained manpower and local entrepreneurs with an average of 0.417, lack of participatory, individual, and social spirit of the local community to invest with a total average of 0.407 and weak domestic and foreign investment with the average of 0.397 were in the next ranks. Providing credit, reducing administrative bureaucracy and political barriers, providing education to villagers and strengthening their participation, as well as attracting domestic and foreign investors to set up small-scale agricultural industries are the main research proposals.
A cana-de-açúcar avança em Goiás, gerando preocupações. Objetivou-se avaliar a expansão do cultivo da cana-de-açúcar em Goiás. Para tanto, na metodologia, realizou-se levantamento de dados sobre a área cultivada, a produção, a produtividade e a receita bruta da cana-de-açúcar por mesorregião no período de 2010 a 2019 junto ao Instituto Brasileiro de Geografia e Estatística - IBGE e Companhia Nacional de Abastecimento - CONAB. Os Resultados demonstraram expansão da lavoura de cana-de-açúcar em todas as mesorregiões do Estado, destacando-se a mesorregião Sul com maior área plantada, produção e receita, junto com a mesorregião Leste com maiores produtividades. Na mesorregião sul, as microrregiões Sudoeste Goiano, Quirinópolis e Meia Ponte e o município de Quirinópolis com maior quantidade produzida. A cana-de-açúcar representa importante receita dentre as culturas na região Centro e está se estabelecendo nas mesorregiões Norte e Noroeste de Goiás. Os impactos locais e regionais são sentidos no aspecto econômico pelas receitas geradas e retorno aos municípios produtores, no aspecto social pela geração de emprego e renda familiar, contribuindo sobremaneira para o desenvolvimento do Estado de Goiás.