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In mid-2020, the University of Liège (ULiège, Belgium) commissioned the ULiège Video Game Research Laboratory (Liège Game Lab) and the AR/VR Lab of the HEC-Management School of ULiège to create a serious game to raise awareness of preventive measures for its university community. This project has its origins in two objectives of the institutional policy of ULiège in response to the crisis caused by SARS-CoV-2 to raise awareness among community members of various preventive actions that can reduce the spread of the virus and to inform about the emergence and progression of a pandemic. After almost two years of design, the project resulted in the creation of SARS Wars, a decision-making management game for browsers and smartphones. This article presents the creative process of the game, specifically the integration of an adapted SEIR (susceptible-exposed-infectious-recovered) model, as well as the modeling of intercompartmental circulation dynamics in the game's algorithm, and the various limitations observed regarding the game's original missions and possibilities for future work. The SARS-CoV-2 video game project may be considered an innovative way to translate epidemiology into a language that can be used in the scope of citizen sciences. On the one hand, it provides an engaging tool and encourages active participation of the audience. On the other hand, it allows us to have a better understanding of the dynamic changes of a pandemic or an epidemic (crisis preparedness, monitoring, and control) and to anticipate potential consequences in the given parameters at set time (emerging risk identification), while offering insights for impact on some parameters on motivation (social science aspect).
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Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.
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The aim of this study is to evaluate the perceptions of prospective tourists through parameters by which the tourism and hospitality service sector can withstand the widespread implications to the sector as a result of the current pandemic. In turn this will lead to weighing up the means for recovery. The identified parameters are then classified, categorized and linked up with supply chain drivers to obtain a holistic picture that can feed into strategic planning from which the tourism and hospitality service sector could utilize to establish a resilient supply chain. This data can provide deep insight for both theorists and practitioners to utilize. It was found that reforming six supply chain drivers, whilst at the same time developing core competencies, is the central essence of a resilient supply chain within the tourism and hospitality business sector (who are at present working hard to counterbalance the many threats and consequent risks posed due to the pandemic).
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Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.
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IntroductionIn the context of the COVID-19 pandemic, which required urgent responses from health systems, and ongoing decision making in a context of limited and evolving evidence, modeling played a significant role in supporting public policy making. Nonetheless, particularly in low and middle-income countries, modeling groups are scarce, and usually not routinely involved in supporting public health policy making. We aimed to appraise COVID-19 modeling work in Brazil during the pandemic.MethodsWe performed a scoping review following PRISMA guidelines to identify groups conducting COVID-19 modeling to support health decision-making in Brazil. Search strategies were applied to MEDLINE, LILACS, Embase, ArXiv, and also included National data repositories and gray literature. We excluded reports of models without modeling results. Titles, s, data repository descriptions and full-text articles identified were read and selected by two reviewers. Data extracted included modeling questions, model characteristics (structure, type, and programming), epidemiologic data sources, main outcomes reported, and parameters. To further identify modeling groups that might have not yet published results, snowball sampling was performed, and a short survey was sent electronically. Investigators and policymakers were invited to an online interview, to obtain further information on how they interacted, communicated, and used modeling results.ResultsWe retrieved 1,061 references. After removing duplicates (127), 1,016 s and titles were screened. From an initial selection of 142 s, 133 research groups were identified, of which 67 didn't meet the eligibility criteria. Of these, 66 groups were invited for an interview, of which 24 were available, including 18 modeling groups from academic institutions, and four groups from State Health departments. Most models assessed the impact of mitigation measures in cases/hospitalization/deaths and healthcare service demand. Interaction and communication with decision-makers were not well established in most groups.ConclusionsDespite a large number of modeling groups in Brazil, we observed a significant gap in modeling demand and communicating its results to support the decision-making process during the COVID-19 pandemic.
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Earthquake Impact Reduction Study for Metro Manila (MMEIRS) estimated that around 40% of the total number of residential buildings within Metro Manila will either collapse or be affected by the M7.2 generated by the West Valley Fault. Need arises to evaluate the seismic vulnerability of structures in the area to enhance the city’s resilience to seismic hazards. Rapid seismic vulnerability assessments are typically conducted by means of sidewalk surveys. However, advances in digital technologies such as Google Street View (GSV) provide the potential to do remote assessments, particularly amid mobility restrictions brought about by the COVID-19 pandemic. This paper aims to demonstrate the usefulness of GSV in collecting data needed for rapid seismic vulnerability assessments through the case of buildings in the City of Manila. Six 300 m x 300 m blocks were evaluated using GSV for identifying seismic-related building parameters. Results show the ease of use of GSV in data collection on areas encompassing commercial and residential zones within the city and poses difficulty for blocks dominated by informal settlements. Among the challenges observed in formal zones include blockages in views due to fences, trees, and/or vehicles parked in front. For informal settlements, much of the buildings are not visible in GSV for evaluation thereby necessitating supplemental data collection. Overall, GSV demonstrates usefulness, and has the potential to speed up seismic vulnerability assessments in urban areas in conjunction with existing in situ assessments currently conducted.
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The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations;so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.
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The outbreak of COVID-19 has attracted people’s attention to our healthcare system, stimulating the advancement of next-generation health monitoring technologies. IoT attracts extensive attention in this advancement for its advantage in ubiquitous communication and sensing. RFID plays a key role in IoT to tackle the challenges in passive communication and identification and is now emerging as a sensing technology which has the ability to reduce the cost and complexity of data collection. It is advantageous to introduce RFID sensor technologies in health-related sensing and monitoring, as there are many sensors used in health monitoring systems with the potential to be integrated with RFID for smart sensing and monitoring. But due to the unique characteristics of the human body, there are challenges in developing effective RFID sensors for human health monitoring in terms of communication and sensing. For example, in a typical IoT health monitoring application, the main challenges are as follows: (1) energy issues, the efficiency of RF front-end energy harvesting and power conversion is measured;(2) communication issues, the basic technology of RFID sensors shows great heterogeneity in terms of antennas, integrated circuit functions, sensing elements, and data protocols;and (3) performance stability and sensitivity issues, the RFID sensors are mainly attached to the object to be measured to carry out identification and parameter sensing. However, in practical applications, these can also be affected by certain environmental factors. This paper presents the recent advancement in RFID sensor technologies and the challenges for the IoT healthcare system. The current sensors used in health monitoring are also reviewed with regard to integrating possibility with RFID and IoT. The future research direction is pointed out for the emergence of the next-generation healthcare and monitoring system.
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Shallow Geothermal Energy (SGE) extracted by Ground Source Heat Pump (GSHP) is a proven clean and profitable technology. Although it is available almost everywhere, its market enjoys different maturity levels along with the other EU Members and even those within the same country. In the Murcia region, in Southern Spain, the presence of GSHP is almost nonexistent. Germany, in contrast, has an extensive tradition of exploiting its SGE resources and is an example of a mature GSHP market. In this work, the technical and non-technical barriers were assessed in both countries to identify the site-specific parameters preventing a better deployment of SGE in Southern Spain. In addition, a SWOT analysis was conducted to highlight the parameters positively and negatively influencing the geothermal resource extraction. Results showed that both study cases showed similar and good technical conditions, such as sufficient resource 80 W/m approx. or a similar impact on the environment mainly due to the use of electricity consumed. However, the regulation and legal framework greatly varied from one area to another. In conclusion, the main factors causing a poor deployment are the lack of specific regulation or regional administration support.
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Demographic probabilities, such as annual survival and harvest probability, are key metrics used in research and for monitoring the health of wildlife populations and sustainability of harvest. For waterfowl populations, annual estimates of these probabilities come from mark-recovery analysis of data from coordinated banding operations. The Brownie model is the most commonly used parameterization for analyzing mark-recovery data from harvested species. However, if banded waterfowl are not released during a year of a multiyear banding operation, then estimating annual survival and recovery probabilities from a dead recovery model is a challenge. Due to coronavirus disease 2019, many wildlife monitoring efforts, including annual waterfowl banding programs, were canceled or reduced during 2020 and 2021, highlighting the need for wildlife managers to better understand the consequences of missing data on analyses and regulatory decisions. We summarized methods of model parameterization and use of alternative methods to explore the behavior of demographic parameter estimates when a year of release data was missing. Comparing constrained fixed-effect models (we set parameters during the missing year of data equal to parameters for years with release data) with random-effect models, we found that 1) bias of estimates during a year of missing release data was smaller when using a random-effect model, 2) the direction of the bias was unpredictable, but the expected range in bias could be generally known commensurate to the underlying variability in survival and recovery probabilities, and 3) potential bias was greatest if the missing year of releases occurred during the final year of a time series. We conclude that in some circumstances, various modeling approaches can provide reasonable estimates during a year of missing release data, particularly when underlying demographic parameters, or the parameter constrained in a model, vary little over time (e.g., adult survival in long-lived species), which would result in relatively little bias in the other estimated parameter (e.g., annual recovery probability). We also suggest that using alternative analytical techniques, such as random-effect models, may improve estimates for the demographic parameters of interest when release data are missing. Random-effect models also allowed us to estimate parameters, such as juvenile recovery probabilities, during the year of missing release data, which are not identifiable using standard modeling techniques. Where accurate and precise parameter estimation is important for making harvest management decisions and regardless of the model type or the data used, there is no analytical replacement for missing release data. We suggest that practitioners determine the potential consequences for missing data through simulation by using empirical data and simulated data with known demographic probabilities to determine the best actions to take for analyzing their capture-recovery data when release data are missing.
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We investigate a class of iteratively regularized methods for finding a quasi-solution of a noisy nonlinear irregular operator equation in Hilbert space. The iteration uses an a priori stopping rule involving the error level in input data. In assumptions that the Frechet derivative of the problem operator at the desired quasi-solution has a closed range, and that the quasi-solution fulfills the standard source condition, we establish for the obtained approximation an accuracy estimate linear with respect to the error level. The proposed iterative process is applied to the parameter identification problem for a SEIR-like model of the COVID-19 pandemic.
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In the early 2020, World Health Organization (WHO) has declared COVID-19 as a pandemic disease. COVID-19 was first discovered in Wuhan, China at the end of 2019. Since the emergence of COVID-19, its spread has been studied by various researchers around the world. The spread of COVID-19 is studied both on the spread between populations and between regions. The spread of disease between regions occurs when a population moves from one area to another. This requires investigation because the spread of the disease can occur from one region to another, so studies of regional factors are needed. In this study, the COVID-19 epidemic model has been established. The model describes the spread of the disease between regions. In the model, the population which is infected with COVID-19 is divided into two categories. The first one is called an asymptomatic patient. Asymptomatic patients are those who are infected or exposed to COVID-19 but have no detectable or significant symptoms in these patients. So that the patient is not considered ill, and the patient can infect healthy people. The second one is called a symptomatic patient. This patient causes symptoms that the patient is exposed to COVID-19. Thus, it is assumed that patient can be given treatment such as quarantine, so the patient is not to be able to transmit the disease to others. It is also assumed that asymptomatic patients can move to other areas and transfer the disease to others, but not symptomatic patients. This study aims to determine the dynamics of the spread of the COVID-19 epidemic with asymptomatic cases between regions and to identify the factors that influence them. The basic reproduction number (R0) of the model is sought to help identify the factors. Partial Rank Coefficient Correlation (PRCC), one of the methods of sensitivity analysis, is also used to identify key parameters that affect the model solution. Numerical simulations are used to show the behavior of the solution.
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The vast Brazilian territory and the accelerated economic growth of the cities of the country’s interior in recent years have created a favourable environment for the expansion of regional aviation. In 2015, the Brazilian Government launched a program of investments in regional airports equipping them to receive commercial flights. However, the economic crisis and the scarcity of resources drive the prioritisation of projects with a greater economic and social return. This article aims to present a multicriteria decision aid (MCDA) model to measure cities’ attractiveness to receive investments in regional airports. The MCDA approach can deal with multiple indicators and different points of view and provide systematised steps for supporting decision-makers. For this purpose, we selected 12 criteria among the evaluation parameters identified in the literature, which led to the construction of the evaluation model and elaborating the ranking of the localities participating in the investment program. This study can contribute scientifically by proposing the use of an MCDA approach to support decisions related to logistics and infrastructure. It can help managers and practitioners provide a structured and systematised model to address decisions related to airport investments.
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The outbreak of the COVID-19 pandemic in 2020 has renewed the interest in epidemic models, striving to infer fruitful information from the available data. The whole world has faced the urge for a sudden comprehension of the spread of the virus and different approaches are nowadays available to cope with the inherent stochasticity of the phenomenon, the fragmentary fashion of usable data and the identifiability problems related to them. This work proposes a novel approach to identify a basic SIR epidemic model with time-varying parameters, where Susceptibles, Infected and Removed (i.e. recovered and deceased) people are accounted for. The standard deterministic approach trivially exploits the average evolution only, disregarding any other information carried out by the epidemiological data. Instead, by suitably formulating a discrete stochastic framework for the mathematical model, the identification task is carried out by exploiting raw data to compute the higher-order moments evolution and involve them in the identification task. The methodology is applied to the Italian COVID-19 case study and shows promising results obtained according to rough epidemic data, essentially provided by the overall amount of contaminated individuals.
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Several studies have estimated the effects of various nonpharmaceutical interventions on the COVID-19 pandemic using a 'reduced form' approach. In this paper, I show that many different SIR models can generate virtually identical dynamics of the number of reported cases during the early stages of the epidemic and lead to the same reduced form estimates. In some of these models, policy interventions effectively reduce the transmission rate;in others, the growth of the reported number of cases slows down even though policy has little or no effect on the transmission rate. Thus, the effect of policy cannot be uniquely determined based on the reduced form estimates. This result holds regardless of whether time series or panel data is used in reduced form estimation. I also demonstrate that the reduced form estimates of the policy effect based on panel data specifications with two-way fixed effects can have the wrong sign.
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The COVID-19 epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.
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This paper investigated the influence and interactions of air pollution concentrations by using the stochastic boosted regression trees between variables for each station and the impact of the COVID-19 Movement Control Order at Ipoh City air quality station. The one-hour data were gathered from the Department of Environment from January until June 2019 and 2020. Two thousand two hundred thirty-one data of particles, gases (Nitrogen oxides, Sulphur Dioxide, Ozone, Carbon Monoxide) concentrations and meteorological data (wind speed, wind directions, temperature, and relative humidity) were captured. The BRT model development process with an algorithm using a comprehensive package, R Software and its packages to understand the variability and trends. It was found that the relationship between the number of samples and number of trees (nt) of 4372 for oob were found the best iterations obtained. The performance of the boosting model was assessed and found that the FAC2 was 0.91, the R2 values were above 0.56 (R = 0.74), and the Index of Agreements (IOA) was 0.67, which fall ranges are within an acceptable for model performance. The Relative Variable Importance (RVI) that influenced PM2.5 for non-MCO data was CO (18.9% ), SO2 (14.6 %), O3 (12.9 %), and wd (10.66 %) while CO (22.6%), RH (13.4%), 14.7% and O3 (12.1%) were RVI factors influenced to PM2.5 concentrations during MCO periods. Estimating the strength of interaction effects (SIE) between variables was 0.24 for CO-wind directions, followed by 0.19 for ozone-wind speeds and 0.15 for NO2-CO. Results showed that the model developed was within the acceptable range and could be used to understand particles and identify important parameters that influence particle concentrations.
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This study focuses on suitable site identification for constructing a hospital in Malacca, Malaysia. Using significant environmental, topographic, and geodemographic factors, the study evaluated and compared machine learning (ML) and multicriteria decision analysis (MCDA) for hospital site suitability mapping to discover the highest influential factors that minimize the error ratio and maximize the effectiveness of the suitability investigation. Identification of the most significant conditioning parameters that impact the choice of an appropriate hospital site was accomplished using correlation-based feature selection (CFS) with a search algorithm (greedy stepwise). To model the potential hospital site map, we utilized multilayer perceptron (MLP) and analytical hierarchy process (AHP) models. The outcome of the predicted site models was validated utilizing CFS 10-fold cross-validation, as well as ROC curve (receiver operating characteristic curve). The analysis of CFS indicated a very high correlation with R2 values of 0.99 for the MLP model. However, the ROC curve indicated a prediction accuracy of 80% for the MLP model and 83% for the AHP model. The findings revealed that the MLP model is reliable and consistent with the AHP. It is a sufficiently promising approach to the location suitability of hospitals to ensure effective planning and performance of healthcare delivery.
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Purpose>Biocontaminants represent higher risks to occupants' health in shared spaces. Natural ventilation is an effective strategy against indoor air biocontamination. However, the relationship between natural ventilation and indoor air contamination requires an in-depth investigation of the behavior of airborne infectious diseases, particularly concerning the contaminant's viral and aerodynamic characteristics. This research investigates the effectiveness of natural ventilation in preventing infection risks for coronavirus disease (COVID-19) through indoor air contamination of a free-running, naturally-ventilated room (where no space conditioning is used) that contains a person having COVID-19 through building-related parameters.Design/methodology/approach>This research adopts a case study strategy involving a simulation-based approach. A simulation pipeline is implemented through a number of design scenarios for an open office. The simulation pipeline performs integrated contamination analysis, coupling a parametric 3D design environment, computational fluid dynamics (CFD) and energy simulations. The results of the implemented pipeline for COVID-19 are evaluated for building and environment-related parameters. Study metrics are identified as indoor air contamination levels, discharge period and the time of infection.Findings>According to the simulation results, higher indoor air temperatures help to reduce the infection risk. Free-running spring and fall seasons can pose higher infection risk as compared to summer. Higher opening-to-wall ratios have higher potential to reduce infection risk. Adjacent window configuration has an advantage over opposite window configuration. As a design strategy, increasing opening-to-wall ratio has a higher impact on reducing the infection risk as compared to changing the opening configuration from opposite to adjacent. However, each building setup is a unique case that requires a systematic investigation to reliably understand the complex airflow and contaminant dispersion behavior. Metrics, strategies and actions to minimize indoor contamination risks should be addressed in future building standards. The simulation pipeline developed in this study has the potential to support decision-making during the adaptation of existing buildings to pandemic conditions and the design of new buildings.Originality/value>The addressed need of investigation is especially crucial for the COVID-19 that is contagious and hazardous in shared indoors due to its aerodynamic behavior, faster transmission rates and high viral replicability. This research contributes to the current literature by presenting the simulation-based results for COVID-19 as investigated through building-related and environment-related parameters against contaminant concentration levels, the discharge period and the time of infection. Accordingly, this research presents results to provide a basis for a broader understanding of the correlation between the built environment and the aerodynamic behavior of COVID-19.
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Cultivation soil is the basis for cabbage growth, and it is important to assess not only to provide information on how it affects the growth of vegetable crops but also for cultivation management. Until now, field cabbage surveys have measured size and growth variations in the field, and this method requires a lot of time and effort. Drones and sensors provide opportunities to accurately capture and utilize cabbage growth and variation data. This study aims to determine the growth stages based on drone remote estimation of the cabbage height and evaluate the impact of the soil texture on cabbage height. Time series variation according to the growth of Kimchi cabbage exhibits an S-shaped sigmoid curve. The logistic model of the growth curve indicates the height and growth variation of Kimchi cabbage, and the growth rate and growth acceleration formula of Kimchi cabbage can thus be derived. The curvature of the growth parameter can be used to identify variations in Kimchi cabbage height and its stages of growth. The main research results are as follows. (1) According to the growth curve, Kimchi cabbage growth can be divided into four stages: initial slow growth stage (seedling), growth acceleration stage (transplant and cupping), heading through slow growth, and final maturity. The three boundary points of the Kimchi cabbage growth curve are 0.2113 Gmax, 0.5 Gmax, and 0.7887 Gmax, where Gmax is the maximum height of Kimchi cabbage. The growth rate of cabbage reaches its peak at 0.5 Gmax. The growth acceleration of cabbage forms inflection points at 0.2113 Gmax and 0.7887 Gmax, and shows a variation characteristic. (2) The produced logistic growth model expresses the variation in the cabbage surface model value for each date of cabbage observation under each soil texture condition, with a high degree of accuracy. The accuracy evaluation showed that R2 was at least 0.89, and the normalized root-mean-square error (nRMSE) was 0.09 for clay loam, 0.06 for loam, and 0.07 for sandy loam, indicating a very strong regression relationship. It can be concluded that the logistic model is an important model for the phase division of cabbage growth and height variation based on cabbage growth parameters. The results obtained in this study provide a new method for understanding the characteristics and mechanisms of the growth phase transition of cabbage, and this study will be useful in the future to extract various types of information using drones and sensors from field vegetable crops.