In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques  offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms , these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field. Crop yield losses because of biotic and abiotic stresses affect roughly 10–30% of global food production ,3. With Traditionally, plant disease detection has been conducted through manual field surveys. These visual disease assessment practices have been criticized due to a large subjectivity in the diagnosis and reliance on clearly visible symptoms often only present in late-stage disease spread . In situRS is generally defined as the measurement of objects at remote distances to acquire useful information indirectly by radiance or irradiance of electromagnetic energy from the surface of the Earth . RS techMost of the literature in the early detection of plant disease had focused on MSI and HSI techniques due to the maturity and implementations of these technologies. MSI and HSI have been applied to many kinds of crops to detect diseases. These two methods concentrate on the differences in the spectral signatures of infected and healthy plant leaves. For some crop diseases, early detection is important for farmers to yield the products economically and efficiently. As there are typically no visible symptoms at early stages of most diseases and fungi located on abaxial leaves, most visible sensors cannot detect early stages of diseases because the signatures are not sufficiently differentiated.Plant health monitoring for precision agriculture encompasses the classification and quantification of the disease. Classification can be divided into two levels of increasing complexity: Detection and identification . DetectiSensors’ sensitivity to these various factors determine the effectiveness and accuracy of disease classifications to the extent of pre-symptomatic detection, as demonstrated in the literature . PhysiolThe use of pigmentation as a plant health indicator is extensively used by in situ sampling methods, as well as some RS applications, as they directly relate to the photosynthetic and physiological properties. Electromagnetic radiation wavelengths are absorbed by pigments, water and biochemicals, resulting in a reduction of reflectance in corresponding spectral regions .From the deviations in the visible and reflected infrared spectrum, plant health can be correlated and related back to abiotic and biotic stresses. In the visible infrared (VIS) between 400–700 nm, the reflectance is primarily correlated to the photosynthetic pigments like chlorophyll, (a and b), xanthophylls, anthocyanins and carotenoids . BroadlyThe assessment of plant’s resistance to stress is very desirable from a breeding perspective. Traditional methods of plant breeding are complex, extremely time-consuming and labour-intensive . The selBeyond health monitoring, RS techniques in fusion with GPS and GIS are used to monitor soil-related stressors and assist with land management. Crop yield can be effectively related to the soil conductivity from which the electrical conductivity can be projected from bands in VIS at 498 nm, 501 nm, 600 nm, 603 nm, 636 nm, 639 nm, 642 nm, 666 nm and 669 nm and in the NIR at 738 nm, 741 nm, 744 nm and 747 nm ,30,31. TIncreasingly, studies are investigating RS applications for soil analysis utilizing MSI and HSI. Notably, using a UAV platform and HSI, a study demonstrated a method for field-scale soil salinity monitoring, a major indicator of soil degradation and arable land loss, and outperformed the analysis conducted using satellite based MSI . UAV-basThe practices of reducing soil erosion, enhancing water management and improving crop yields are crucial concerns in conservation agriculture. Three crop management practices—lowest possible soil disturbance, permanent soil cover and crop rotations—are proposed by conservation agriculture based on the Food and Agriculture Organization (FAO) definition . The ferThe relationship between the collected data and classification of plant disease is usually characterized by a vegetation index (VI), as outlined in The plant health assessment from spectroscopic sensors focuses on correlating the reflectance to photosynthesis to assess health. Therefore, the plant health assessment heavily relies on being able to take accurate measurements from the leaves. Understandably, in RS, the reflectance of the leaf can be influenced by the angle of leaf relative to the sensor, the ambient lighting spectrum and the intrinsic surface characteristics of the leaf itself. Inherently, the viewing angle and angle of illumination affect the brightness of the canopy, and subsequently, the crop identification, LAI and disease detection. The use of the canopy’s bidirectional reflectance distribution function (BRDF) can help to analyse the measurements made at non-optimal angles considering the surface characteristics of leaf . The BRDtF [In practise, the BRDF model is comprised of an equation correlating the surface physical properties to the observed reflectance as the light is subjected to scattering due to the canopy structure as depicted in ance, Ft .(1)ρ can detect bacteria at 100 Colony Forming Units (CFU) per mLorganism ,43. The organism .Nucleic acid-based methods are DNA- or RNA-based detection methods that generally rely on polymerase chain reaction (PCR) variant techniques . An examFluorescence spectroscopy is considered a cost-effective, fast and sensitive method . The priFluorescence is particularly sensitive to external light, impacting in-field applications . HoweverEmerging RS techniques rely on detecting anomalies in photosynthetic parameters. These alterations in pigment, chemical concentrations, nutrient, water uptake, cell structure and gas exchange can then be observed by the subsequent change in reflectance characteristics of the leaf or canopy . The anoParametric analyses of spectral signatures of diseased plants are not common. Instead, researches have, for the most part, implemented nonparametric methods such as the Principal Component Analysis, Support Vector Machines, Cluster Analysis, Partial Least-Squares and Neural Networks . A samplMultispectral imagery (MSI) uses sensors that measure reflectance across a broad band of the electromagnetic spectrum. The broad band from MSI sensors is composed of 3–10 distinct band measurements in each pixel from the images the sensor gathers . In contHyperspectral sensors  are the next generation in spectral imaging, surpassing MSI radiometers prevalent in notable systems like LANDSAT, SPOT and IKONOS. These MSI examples are often used as the baseline to determine the accuracy of a HSI system. The information drawn from HSI is referred to a hyperspectral image data cube.The HSI sensor continuously collects multiple images across numerous wavelength bands such that the spatial location (the X and Y coordinates correspond to the ground dimensions), which is represented by the pixel resolution of the sensor, compiles the data of all the measured wavelengths (the Z axis) . HSI is In the HSI model, different surfaces, such as the tree canopy or food crops, are represented by first- and second-order spectral statistics. The first- and second-order statistics of the spectral reflectance of each surface type can then be transformed in the spectral image process. In this process, the mean vector and covariance matrix of each surface reflectance type or class are used to evaluate the system’s performance. The HSI model is composed of the image condition module, the sensor module and the processing module.X is the vector of the surface reflectance, The image condition module describes the radiation transfer in the atmosphere as this accounts for the absorption, reflection and scattering that will affect the HSI performance. As a result of these influences, the radiance at the sensor, In the sensor module, the incident radiation passes through a detector, is amplified and is then evaluated for all of the spectral and spatial characteristics. The sensor module contains a spectral model, a spatial model and an error model.A, the number of bands, σ, and the weight matrix, The mean and covariance of the spatial model is related to the reduction in radiation and image degradation based on the coefficient of radiation reduction, B is a linear transformation matrix of the spectral response functions.The spectral model contains the centre wavelength as well as the adjacent wavelengths radiation . The effThe error model consists of noise sources such as quantification error; dark-current noise, These mean and covariance statistics for the three main models make up the spectral signal statistics from which the data processing can extract useful data.The performance metrics of the HSI system is made up of three algorithms. One compares the spectral characterization accuracy based on the mean difference between the starting known reflectance and the received reflectance of the target. The second compares the probabilities that the sensor makes a detection opposed to a false detection. The third calculates the total error based on the total probabilities of a false alarm or missed detection .In both MSI and HSI, the images are improved by lowering the unwanted intensity and improving the definitions of object boundaries by means of nonlinear diffusion for greater classification accuracy . The nont, and g is the nonlinear diffusion coefficient, the goal of which is to measure the dissimilarity between image gradients of two vector weighted pixels [V, consisting of vectors, iv, related to the spectral signature ith pixel where xy is the total number of pixels [Here, the resultant smooth image is d pixels . The hypf pixels .(10)V=[I is an identity matrix and G is a function of diffusion coefficients, normalized by the number of spectral bands, zm.The image gradient, which uses a semi-explicit scheme that improves the smoothness and functionality, can then be described accordingly .(11) can achieve the same classification accuracy of the 240 HNB [HSI is disadvantaged by high cost, system fragility and complexity ,69,87. T 240 HNB . The opt 240 HNB . ResearcA review of the application of neural networks to HSI data was conducted, demonstrating the accuracy in disease detection by HSI . The remIn disease detection, HSI is clearly advantageous in being able to acquire large amounts of data from which a lot of useful information can be extracted. However, there is limited maturity in the image processing systems, which limits feature extraction. Hence, few good automated systems are found in the literature . AdditioThermography is a passive technique in plant disease detection which exploits surface temperature measurements of leaves/crops/canopies to identify deviations indicative of abiotic or biotic stress. Thermographic cameras target the thermal infrared spectra, converting the pixels into temperature values, and the relative condition is then related to the leaf transpiration and water content . Thermog2 Emissions over Nights, Days and Seasons (ASCENDS) mission. In this case, Differential Absorption LIDAR (DIAL) made it possible to the gap in knowledge of oceanic carbon sinks at night [Active measurement systems like LIDAR are suggested as an alternative to the shortcomings of passive systems ,106, as at night . As LIDAat night . Additioat night .Practical difficulties of in-field implementations such as complex canopy structures, colour variations, textures, lighting conditions, shadows and heavy post-processing costs are key limitations of VIS-based crop monitoring systems, which have led to the development of a dual LIDAR UGV-based system acting in a pseudo stereo vision as an alternative low-cost, high-accuracy system . Hypersp2 concentration over the canopy. Ambient CO2 over canopies does not change in the early morning or at night, whereas it decreases significantly at midday (about 12 pm) with a changeable amount of 411−2.5 = 408.5 ppm [2 absorption from plants. The amount of absorbed CO2 is maximized by healthy plants. However, the amount of absorbed CO2 is reduced by stressed plants. Sensors are required to measure these small changes. Stressed plants are caused by a lack of water, excess of fertilisers, increased salt in the root zone and diseases. In addition, CO2 absorption consumed by plants is necessary to generate food and energy for their growth and cellular respiration. In field conditions, CO2 concentration over or in the canopy is extremely dynamic due to the diffusion and turbulence processes.Using DIAL, it is possible to detect nonvisible symptoms of agricultural diseases based on the CO08.5 ppm . Therefo2 is becoming significant in various agriculture applications, e.g., CO2 soil respiration, gas exchange, observing atmospheric gas, in the alcohol and beverage industry [2 concentration detection is a powerful invention which we can apply to measure the anomalies of CO2 concentration associated with stressed plants. In 2 absorption are used to discern plant health.Carbon dioxide sensors for the agricultural sector have a few distinctions among the conventional industrial sensors regarding the determination of environmental parameters. The sensors are essential for performance under extraordinary conditions of temperatures, pressure and humidity. The agricultural environment is difficult to completely define, with numerous microorganisms and other biological species impacting the parameters under study . The agrindustry , the scaindustry  and the industry . The sen2 concentration in fields are extremely complex based on the factors of soil respiration, plants photosynthesis and air turbulence. The CO2 concentration changes of soil and plants are much easier to detect than air turbulence changes. However, LIDAR techniques can solve air turbulence problems with a remarkably fast measurement process.Variations of COA typical LIDAR shape profiling system  architecture is shown in The LIDAR sensor is used to measure the relative position of an object in five dimensions, which are the x, y (ground dimensions) and z (height above canopy) coordinates and the time and the intensity of the reflected light by which laser source emits light and the detector receives the reflected light . In factSeveral vegetation applications primarily based in laboratory conditions have been improved using multispectral laser systems. The dual-wavelength Spectral Ratio Biospheric LIDAR, which indicates the modifications of ratio between the red and near-infrared wavelength regions from a phenology cycle of a tree canopy, was studied by Rall and Knox . The Mul2 and O2 concentrations. The approach allows for greater flexibility, is less wavelength-dependent, removes the scattering effects of thin clouds and surface scattering and is also used in conjunction with the DIAL technique [The integrated path differential absorption (IPDA) LIDAR technique transmits (at least) two laser pulses with similar wavelengths, as demonstrated by NASA’s space-based ASCENDS mission platform to detect COechnique .2 concentrations associated with photosynthesis and respiration [LIDAR absorption spectroscopy systems have been proposed as a feasible method to remotely sense the atmospheric components as the laser beam at the detector can discern the characteristics of the medium the beam passed through . The usepiration ,134.The bistatic LIDAR system consists of a transmitter that emits a continuous wave or pulsed laser beam of precisely known power characteristics. The amount of laser energy that arrives from the transmitter, which is less than the emitted LIDAR radiation because atmospheric molecular/aerosol concentrations absorb or scatter the radiation , aerosol absorption (aα) and aerosol scattering (aβ) are also ignored due to DIAL measurement principle. The molecular absorption  in cm Rx(λON), and for the non-master wavelength, PRx(λOFF), in a direct path can be calculated as [The scattering coefficient is made up of i et al. . Molecullated as :(17)RON/2 concentration (Using the scattering coefficient in Equation (3), the net CO2 concentration. Various DIAL systems can be integrated with RS systems due to the emergence of lightweight, powerful laser sources and systems. Active monostatic configurations are employed with DIAL configurations for atmosphere sounding that measures not only the elastic backscatter produced along the external path but also the radiance of the illuminated Earth surface in the direction of the airborne system. In addition, numerous DIAL systems have been improved to measure the concentration/column density of different significant molecule species based on the development of powerful tuneable lasers [The DIAL measurements mainly focus on molecules vibrational absorption associated with laser wavelengths. This signal analysis enables an estimate of the COe lasers ,151,152.The performance of LIDAR is underpinned by the attenuation of the laser beam through the atmosphere based on the characteristics of the atmosphere and the power and wavelength of the beam. At low output powers, these propagation behaviours are linear, while at sufficiently high output powers, the behaviours are nonlinear . The linMolecular line absorption is a dominant cause of attenuation dependence on the laser wavelength. In this case, the laser encounters one  molecular species at the specified wavelength as it propagates through the air. The energy from the laser is then attenuated by the molecular species in its path. Knowing how the wavelength corresponds to a certain molecular species (high-resolution data is available in programs such as HITRAN), the attenuated energy can be calculated and used to determine concentrations of various molecular species present in the medium.Continuum absorption describes a type of molecular absorption as a result of molecular clusters. A part of this type of attenuation is atmospheric scattering, where a directional change leads to a reduction in the beam intensity, especially over large paths. There are different types of scattering, which are characterized by the physical size of the scatter. In small air molecules, attenuation leads to Rayleigh scattering, and for aerosol-sized molecules, it leads to Mie scattering. For even larger-scale molecules, the scattering can be described by diffraction.e, the natural frequency, w0, and a damping coefficient. Mie scattering considers the case where the wavelength of the laser beam is scattered by particles of similar sizes. The size, dielectric constant, shape and absorptivity of the particle is accounted for in Mie scattering. To calculate the atmospheric attenuation coefficient, it is important that the quantities of all the different sizes of aerosol particles are known, which can be difficult. Mie scattering is dependent on atmospheric conditions such as humidity [K, and the Mie attenuation coefficient. Propagation through the effects of precipitation is significantly attenuated and is dependent on the specific atmospheric condition, which the authors of [Rayleigh scattering often has a much larger effect on the propagation than molecular absorption in the VIS and NIR and is characterized by the scattering cross section in the equation below .(19)σs=humidity .(20)−dIthors of  exploredThe beam is also subjected to propagation through atmospheric turbulence where turbulence energy is introduced in large scales. Turbulence is inherently nonhomogeneous and non-isotropic, making it difficult to model with dynamic vertical profiles dependent on weather factors such as wind speed, pressure, temperature and humidity. In describing beam attenuation from turbulence, the refractive index structure coefficient is considered as the most important parameter . The coeThe turbulence effects on the laser beam, illustrated in These types of attenuation are linear as the laser beam does not affect the air it passes through. However, for beams with high irradiance, attenuation induces thermal changes in the air it passes through, resulting in nonlinear effects as the temperature affects the density and index of refraction, in turn altering the beam’s irradiance distribution . These tP, the attenuation coefficient, γ, and the contributions of diffraction, jitter and turbulence given by The system performance at peak irradiance for nonlinear effects such as atmospheric turbulence, random jitter, thermal blooming and diffraction is given in terms of the output power, Additionally, aerodynamic effects can produce optical anomality’s as a result of the viscous flow of laminar and turbulent boundary layers, as well as the inviscid flow. In the mid-IR, turbulent surface boundary layers are likely to cause optical aberrations for high-powered lasers. In this case, increases in the Mach number and Reynolds number result in more complex interactions and optical aberrations for airborne laser systems .As discussed, the canopy scale introduces variables that degrade sensors’ ability to distinguish important relationship between the reflectance spectrum and plant health. The combined use of passive HSI and active LIDAR has demonstrated several benefits, enabling the system to filter out peripheral spectral information in the canopy. The analysis of pigmentation was shown to improve above the canopy with the addition of LIDAR  by utiliThe trends shown in research show that the identification of molecular gases in LIDAR applications is of research interest for both HSI–LIDAR data fusion in agriculture applications. The most recent developments have used the laser propagation principles in Ramen scattering to identify and quantify nitrogen and oxygen molecules as a means of determine atmospheric composition. Most applications of HSI–LIDAR data fusion focus on LIDAR 3D scanning technology as means of calculating tree height for biomass estimation. This approach underutilizes the data that could be retrieved by the LIDAR sensor, which can perform similar plant health analysis as HSI, through an understanding of the laser propagation properties of LIDAR. This approach makes the LIDAR sensor much more versatile in a precision agriculture application, and combining the LIDAR sensor with the HSI technique would mitigate some of the shortcomings of the HSI, such as variations in the canopy that BRDF techniques have to be used to compensate for. Further research into in-field LIDAR-based chemical detection is required to fully exploit any potential data fusion, but there are clear benefits of increased accuracy by taking the reflectance based (HSI) and molecular absorption based (LIDAR) combination in the context of challenging environments in agriculture applications.Fluorescence spectroscopy was used to estimate the ripeness of mandarins ,159. HerBy incorporating MSI data obtained from a UAV with a handheld fluorescence spectrometer, researchers were able to assess grape maturity, acidity and sugar content . A compaBeyond disease classification and quantification, HSI is increasingly used to assess food quality under constrained laboratory conditions . In comparison to MSI, HSI is especially useful in identifying the optimal wavelengths ,99 to coLIDAR sensor applications in fruit quality are currently focused on counting and tracking the fruit. A unmanned aerial vehicle (UAV)-based platform utilizing a multi-sensor approach of LIDAR, MSI, thermal imaging and navigational sensors was used to extract plant health data  includinTrue exploitation of RS for precision agriculture invariably lies with the application to a canopy scale to fully appreciate the spatial information advantages, especially for large agriculture areas. Whereas the in situ and RS sensor technologies have been extensively tested in heavily controlled circumstances, in-field airborne applications are increasingly demonstrating their usefulness in time saving both in processing and for large areas, spatial information and accuracy. One potential drawback is the diminishing resolution of MSI, HSI and LIDAR sensors as the distance between the sensor and target area is increased. However, this can be actively managed as part of the flight mission planning. Gleaning the reflectance of the entire canopy also introduces factors such as the variation in leaf layers (LAI); the orientation of the leaves, called the leaf angle distribution (LAD); weather; shadows and non-leaf elements like soil . These fThe prevalence of agricultural robots or unmanned ground vehicles (UGVs) is notable in applications such as seed identification, field scouting and harvesting ,176,177.Using a similar approach, utilizing 3D LIDAR in the mapping of forest areas on a UGV platform demonstrated the ability of UGV platforms in uneven, complex terrains while providing an efficient approach for the calculation of tree locations and diameters . UGVs su2 column absorption capabilities on ground-based, aerial and space-based platforms [UAV, aerial and satellite RS platforms for plant disease detection are continuously improving their spatial and temporal resolutions, thus increasing the number of suitable applications for precision agriculture. The distinction between platform suitability is heavily biased toward the mission specificity, spatial scale and cost. The advantages of each remote sensing platform are summarized in latforms . Both aeUAVs have been demonstrated to be competitive, with more well-tested imagery acquisition platforms such as aerial and satellite in large part due to low operational cost, high resolution and mission flexibility. The flexibility of UAV was highlighted for vineyard diseases that occur in small heterogenous areas, which are observable only for brief periods . DetectiUAV-based platforms in precision agriculture have been used and validated for the detection of water stress , biomassPlant disease detection techniques are driven by governing body regulations such as the European and Mediterranean Plant Protection Organisation (EPPO) to avoid crop yield losses. An example of this work demonstrates how MSI data from a UAV platform can be combined with proximal sensors to assess fruit quality . The higUGVs are advantageous over other platforms in their ability to carry many sensors that can be heavier and bulkier than UAV applications which are used for high-resolution data acquisition. Conversely, the UGVs struggle in complex unstructured environments, are usually slow moving and are hindered by ground obstacles. The research suggests a trend toward cooperative UGV and UAV systems for improved coverage and resolution. In this model, the UGV performs complimentary data acquisition and acts as a mobile charger for the UAV, delivering it to deployment locations as seen in Adopting the right combination of sensors, platforms and data analytics in agriculture helps reducing the proportion of manual operations and leads to improved yields through the effectiveness and efficiency gains that only wide-area remote measurements can achieve. This ongoing evolution is increasingly driven by the application of AI to perform nonstandard tasks, which expand the range of activities that can be performed in precision agriculture to allow for data-driven decision-making from the operators. For instance, it is functionally essential for RS sensors such as HSI, MSI and LIDAR to utilize machine learning (ML) techniques to sort, segment and classify the large amount of data acquired. Correlating the spatially and temporally distributed data from one or more sensors to definitive classifications and diagnoses indicating plant health is achieved through a variety of different ML techniques, typically tailored to the specific crop, conditions, sensor and platform. ML in precision agriculture has expanded with increased sensor resolution and has been paired with modern algorithms to allow for multi-data fusion. AI techniques are used in two distinct ways in remote sensing platforms: For mission control  and for processing the data from the sensors into interpretable information for the operators. The methods used to filter the raw spectral data and quantify it into statistical data from which disease classification can be inferred greatly influence the accuracy of the system and warrant further exploration. As observed in the literature see , numerouFuture work in data analytic methods for precision agriculture should examine AI data fusion, and the use of digital twins and meta-reasoning as methods to improve the accuracy and efficiency of RS systems. The observed research clearly trends toward a multiple sensor data fusion approach in the health monitoring of crops. The use of appropriate techniques to collate the data from different sensors in order to improve the classification accuracies is of significant interest. As observed in the section concerning HSI–LIDAR (3D scanner) data fusion, the data collected does not really overlap. The HSI system measures the reflectance, which is used to discern information about the photosynthetic efficiency, and the LIDAR 3D scanner usually measures the heights of the crop to measure biomass. The combination of these results provides more information. However, both HSI and bistatic LIDAR are a measure of photosynthetic efficiency, and with their combination, there is opportunity to share information between the sensors to better improve each technique.The application of meta-reasoning to the data analysis techniques is an emerging technique designed to measure the efficiency of the data analysis. Meta-reasoning examines and controls the resources and time that the data analysis takes for each task. This optimizes the decision-making and quality of the result.Partial Least Squares Regression (PLSR) uses a linear multivariate model to relate and model the structure of two data matrices . PLSR acPCA is used as predictive modelling tool in correlating large sets of data. PCA reduces the dimensionality of the data and minimizes data loss by the creation of uncorrelated variables the maximize variance . The PCAPCA is a common data analysis method in plant disease detection. The PCA method was used in the detection of diseases in the Catharanthus roseus leaves, which highlighted the benefits of PCA as an unsupervised clustering method that does not require a priori data set knowledge . ResearcvW is the weight vector, s is the iteration, θ is the neighbour function, α is a learning restraint and D(t) is input vector.A Self-Organizing Map (SOM) is an Artificial Neural Network (ANN) that takes high dimensional data and transforms it into a 2D map that places similar data points closer together . As in mSOM has been used in data fusion applications to reduce the disease classification error of yellow rust in wheat using MSI and HSI sensors . The anaσ(γ) to obtain the output.ANN is a supervised learning technique that consists of a neuron net structure originally aimed at mimicking human brain problem-solving. Due to its structure, ANN is used to infer functions from the observed data, which learns a pathway to the output. In this example, the input can take multiple pathways to output with the hidden component, a weighted function  that uses a priori knowledge. The inputs, p, to the neuron are multiplied by a weight, w, the sum making the bias weight, then passed through a transfer function ANN is used in the classification of fungal disease and other common anomalies in rice plants ,203, theAlong with ANN, SVM is a typical algorithm used to classify plant diseases from the feature extraction process depicted in b is the bias, i indexes the n training data and w is normal to the hyperplane.SVM is a supervised technique that uses given sets data of at least two classes. With each new data set, the SVM measures the distance between the new points and the given data to classify it. A main variant of SVM is hard margin classifiers, which are especially useful in multi-dimensional data applications such as HSI and MSI. The SVM classification uses parallel hyperplanes based on the biggest margin between the hyperplanes.SVM has been widely adopted in the literature due to its proven performance in highly dimensional problems with small data sets . The autK-Nearest Neighbours (KNN) is a widely used classification method with a learning algorithm based on the similarity of data points. Using a training dataset, once the algorithm makes the classification and groupings, the algorithm training is complete. This is a nonparametric algorithm, where the closeness to a number of known K points and voting process are used to classify the new data. Euclidean distance function is a common method to evaluate the closeness to the nearest pointsThe KNN is simple and easy to implement. However, the method becomes inefficient when analysing large amounts of data.Regions of interest (ROI) segmentation is used to define the borders or boundaries of an object and to describe an area in a compartmentalized way. ROI segmentation is essential for applying machine learning techniques for plant disease detection. In one study, researchers investigated the ROI applications based on colour variance between healthy and diseased parts of the plants . The stu2 absorption measurements, is an area that requires further research to be able to improve early plant disease detection, soil analysis, phenotyping and fruit quality analysis. These multi-sensor systems will adopt AI-based data fusion algorithms to efficiently and accurately process the variety of spatially and temporally distributed data acquired in the field.This review examined current and likely future electro-optical remote sensing applications for precision agriculture, with a focus on novel methods for early detection of plant diseases and the increasing adoption of spectral analysis in food quality assessment. The way in which plant diseases spread and cause subsequent deterioration of the plant determines the efficacy of each of the detection techniques discussed. For in situ applications, the sensitivity of thermography and fluorescence to ambient light reduces the accuracy and practicality of these techniques. Techniques reliant on spectral information must undergo a complex analysis to eliminate erroneous data due to the complex interactions with the environment. In consideration of the discussed techniques and their advantages and disadvantages, the evidence clearly points to a data fusion approach that combines multiple spectral techniques to improve the classification accuracy and to reduce the effects of external/environmental factors impacting the quality of data collected. Whereas the narrow band approach of HSI allows greater flexibility in distinguishing spectral areas of interest, the HSI sensor is particularly susceptible to erroneous data due to data overlap and is a costly/complex solution. The MSI system, in contrast, requires greater understanding of the spectral bands of interest before implementation. The added value of emerging LIDA-based systems is in their greater flexibility in capturing data as LIDAR systems are not as limited by atmospheric conditions, changes in light, viewing angle or canopy structure. The implementation of a low-cost, lightweight bistatic LIDAR system on a RS platform should be considered for future research, especially in the context of HSI-LIDAR data fusion. LIDAR is noted for its wide-ranging applications across all sensor platforms and, in combination with hyperspectral imaging, would likely be the most robust of the remote sensors. Driven by market demands, future work should be aimed toward food quality analysis using various combinations of remote sensors. The greatest hinderance for RS techniques is their indirect nature in correlating spectral or molecular data to a definitive diagnosis. The adoption of AI techniques for the spatially and temporally distributed big data acquired by RS is one of the biggest impacting factors on the accuracy of plant disease diagnosis. Comprehensive comparative studies of AI-based data fusion techniques in the precision agriculture context are needed to determine the best combination of sensors, platforms and crops, especially in the multi-sensor approach. Among the various sensor platforms, UAVs clearly fill a gap between the limitations of handheld and rover-carried sensors in large areas and of high-altitude and satellite sensor resolutions. The overall accuracy and resolution of UAV based sensors in precision agriculture should be further investigated, emphasising the disparities between leaf level and canopy level data acquisition. A multi-sensor approach, taking advantage of both traditional techniques and the emerging benefits of LIDAR-based in-field CO


Non-invasive fetal electrocardiography represents a valuable alternative continuous fetal monitoring method that has recently received considerable attention in assessing fetal health. However, the non-invasive fetal electrocardiogram (ECG) is typically severely contaminated by a considerable amount of various noise sources, rendering fetal ECG denoising a very challenging task. This work employs a deep learning approach for removing the residual noise from multi-channel fetal ECG after the maternal ECG has been suppressed. We propose a deep convolutional encoder-decoder network with symmetric skip-layer connections, learning end-to-end mappings from noise-corrupted fetal ECG signals to clean ones. Experiments on simulated data show an average signal-to-noise ratio (SNR) improvement of 9.5 dB for fetal ECG signals with input SNR ranging between −20 and 20 dB. The method is additionally evaluated on a large set of real signals, demonstrating that it can provide significant quality improvement of the noisy fetal ECG signals. We further show that employment of multi-channel signal information by the network provides superior and more reliable performance as opposed to its single-channel network counterpart. The presented method is able to preserve beat-to-beat morphological variations and does not require any prior information on the power spectra of the noise or the pulse location. The fetal electrocardiogram (ECG) can be used to monitor the condition of the fetal heart from early pregnancy until delivery . NowadayNon-invasive fetal electrocardiography, performed by placing electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. In comparison with cardiotocography, it provides more accurate information because it does not need to average over multiple beats for the heart rate extraction. Moreover, it provides the possibility to assess the ECG morphology, related to the electrical activity of the fetal heart. The advantage of the method over the scalp ECG measurements is that it can be performed already during pregnancy, it is safe for the fetus and comfortable for the mother. However, the difficulty to extract a clean fetal ECG from the abdominal mixture is the main reason that the application of the method in clinical practice is still limited. The interferences and noises in the abdominal recordings among others include the maternal ECG, powerline interference, baseline wander, muscle noise from the fetus and mother and movement artifacts. Considering that the signals of some of these interferences overlap both in time and frequency with the fetal ECG, the extracted fetal ECG signals usually have very low signal-to-noise ratio (SNR). Therefore, the non-invasive recordings are in practice merely used for heart rate analysis.There are typically three main steps in the fetal ECG extraction process; preprocessing, separation and postprocessing . PreprocRecently, deep neural network models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and stacked denoising autoencoders have been successfully applied for a variety of purposes including signal and image denoising –13. MoreIn this work, we are dealing with the aforementioned problem by extending our model to handle multiple fetal ECG channels. Multiple electrodes measure the electrical activity of the heart from different angles. We propose to use a deep convolutional encoder-decoder network with symmetric skip connections that learns how to optimally combine the input channels to deliver a reliable clean, multi-channel ECG as output. The method eliminates the residual noise in the fetal ECG by capturing the signal structure in the convolutional layers and recovering the details by the transposed convolutional layers.For the training, but also for the evaluation of the proposed network, we created an extensive simulated fetal ECG dataset that consists of two parts. The first part was built by employing the fecgsyn toolbox developed by Behar et al. , 23. TheTo further enrich the ECG morphologies in our dataset and reduce the risk of overfitting to the training data we generated an additional set of simulated signals based on adult ECG from the PTB Diagnostic ECG Database of Physionet . The datIn order to evaluate how well our algorithm performs in real signals we employed two databases. The first one is a private set of non-invasive fetal ECG measurements, obtained in collaboration with the Máxima Medical Center, Veldhoven, the Netherlands , 29. TheThe second real dataset is the Abdominal and Direct Fetal Electrocardiogram Database which consists of four-channel abdominal fetal ECG recordings obtained by five women in labor, between 38 and 41 weeks of gestation . Each reThe signals of all the datasets were preprocessed before entering the network. The fetal ECG extraction was performed with the help of the open-source algorithm of Varanini et al.  and the The proposed fetal ECG denoising CNN network is illustrated in The non-invasive fetal ECG typically contains a high amount of noise and thus a large denoising patch can lead to more efficient noise removal by using context information from a larger signal region. It was indicated in the literature that the denoising patch is highly correlated with the receptive field of the network, i.e., the region in the input space that a CNN feature can be affected by , 32. TheIn shallow networks transposed convolutions works well for recovering the signal details but as the network goes deeper, they do not longer work satisfactory . Our netFor training the network the normalized mean squared error loss was minimized, which is defined as:X represents the fetal ECG and X. In our experiments N = 64, L = 4 and M = 1920. The Adam algorithm was selected . The metric was calculated for 455 cases, where sufficient QRS complexes were detected for the generation of the “ground truth” signal.In the Abdominal and Direct Fetal Electrocardiogram Database, since simultaneous scalp measurements are provided together with the non-invasive fetal ECG, the performance of our method was evaluated by comparing with the scalp electrode. The scalp ECG is however a different lead than the abdominal ones and we cannot compare them directly since, even in case of perfect denoising by our method, the morphology of the ECG will not be the same between different leads. Instead, we estimated a denoised scalp ECG as a linear combination of the four abdominal fetal ECG channels:Xscalp is the [1 x 250] scalp ECG and a, were computed on windows of half a second that corresponds to 250 samples. The dimension of Xdenoised is 4 x 250. Because the scalp ECG measurements contain considerable amount of noise and this could affect the comparison, we denoised the scalp ECG by high pass filtering followed by averaging of 30 ECG complexes. Nevertheless, we provided comparative results both when the estimation was done based on the noisy scalp ECG as well as on the denoised scalp ECG.where R), the mean squared error (MSE), the mean absolute error (MAE) and the signal-to-noise ratio (SNR). The metrics are defined by the following equations:Four different quantitative measures were employed for the comparison, the Pearson correlation coefficient , indicating that the single-channel network may provide more outliers, while the multi-channel a smoother outcome.First, we believe that denoising of the scalp ECG was important to allow for better comparison with the scalp ECG estimation from the denoised abdominal leads. By averaging 30 successive ECG complexes we might have lost some morphological variations among the successive beats of the scalp lead but achieved significant quality improvement. Even the scalp ECG approximated by the noisy fetal ECG signals has better resemblance with the denoised scalp lead, e.g., correlation coefficients of 0.74 vs. 0.53. Second, we observe that both the multi-channel and single-channel networks achieve significant quality improvement of the fetal ECG signals for all the metrics presented in We have proposed a CNN network for postprocessing non-invasively extracted multi-channel fetal ECG signals to improve their quality. The non-invasive fetal ECG is substantially contaminated by various noises, even after the application of various signal processing tools proposed in literature, such as maternal ECG suppression. The low quality of the fetal ECG is the principal reason that the applicability of non-invasive fetal electrocardiography in clinical practice is limited. The suggested multi-channel network was trained on a wide dataset of simulated four-channel ECG signals, with SNR ranging from−15 to 15 dB, while it was extensively validated both on simulated as well as on real datasets.Experiments on simulated data showed a significant improvement in the quality of the noise-corrupted fetal ECG signals. The network combined information from all the channels to efficiently remove the noise and uncover the ECG signal morphology even in the presence of acute noise. However, the network suppressed some morphological characteristics in cases there was not sufficient content for denoising i.e., when most signal channels were severely corrupted. The multi-channel network outperformed the single-channel  in casesThe evaluation of our network on a large real fetal ECG dataset showed an analogous behavior to that on the simulated data; for low quality fetal ECG the multi-channel network outperformed the single-channel, while for higher SNR the performances of the networks were comparable. We cannot make a direct comparison because the evaluation method for the real signals was suboptimal. We are aware that the approximation of the ground truth signals with the running average of 100 heartbeats was not very accurate. However, it gave us an indication that the method is efficient in real data too. We additionally presented some qualitative denoising results for two signals of this database in The CNN network was additionally evaluated in the Abdominal and Direct Fetal ECG Database. Simultaneously recorded scalp ECGs were compared to an estimated scalp ECG from the denoised abdominal channels, also here demonstrating that the method can provide significant quality improvement of the noisy fetal ECG signals. Comparison of the performances of the multi-channel and single-channel networks for this database, revealed that they achieve comparable results, probably because the input signals were of relatively good quality. It is difficult to compare the performances between the two real datasets for several reasons. Most importantly, the sizes of the two datasets differ a lot (455 vs. 5) and so do the gestational ages of the subjects (18-24 vs. 38-41 weeks).As mentioned in the introduction and also in , the shoTo summarize, we have shown the potential of deep CNNs for removing noise from non-invasive multi-lead fetal ECG. We validated the method on a wide dataset of simulated but also real recordings with both early as well as late gestational ages (18 to 24 and 38 to 41 weeks). Primarily, we demonstrated that employing multi-channel information for denoising does not only lead to more clean signals but also to more reliable results, when compared to single-channel information. The main advantage of the method is that, as opposed to the widely used averaging method, no prior processing of the signal is needed to extract the locations of the R-peaks and variations in ECG morphology among consecutive heartbeats are preserved. This is especially important in case that arrhythmias are present. Up to now, arrhythmia is assessed through echocardiography because the averaging that was performed to enhance the quality of the fetal ECG hinders its application for arrhythmia analysis. Moreover, the quality of the denoised signals is high enough to allow for measuring the timing of intervals, like the PR and QT interval. However, in order to confirm this, we need to perform a thorough comparison of the ECG intervals between the denoised and the clean signals. If we wish to obtain reliable results, a large annotated dataset is necessary, but this requires time and experts to perform these annotations.Certainly, there is room for improvement of our method. Most importantly, the capacity of the network could be further increased to handle even more noisy signals. Moreover, we can explore denoising directly the raw abdominal signals, without cancelling the maternal ECG. Most probably a more complex network architecture is needed for such a task and appropriate data for training.An end-to-end trained deep CNN network was presented for denoising of fetal ECG signals. Convolutions and transposed convolutions were combined in the network, modeling the denoising problem as an encoding of primary signal content and subsequent decoding to recover details. Essentially, we proposed to employ spatiotemporal information in the ECG signal by using multiple ECG leads simultaneously as input to the network. The network then learned how to combine the input channels and deliver a reliable clean ECG as output. Experiments on simulated as well as in real data showed that the network can achieve a substantial quality improvement of the noisy signals and outperform a single-channel alternative.j.luime@mmc.nl). Other data used in the study can be found in: https://archive.physionet.org/physiobank/database/adfecgdb/?C=S;O=A.Data are available from the Data Governance Board of the Máxima Medical Centre to researchers who can demonstrate that they are qualified to use confidential data. Requests to access the data should be addressed to the corresponding author or the Data Governance Board at the Máxima Medical Centre . Participants were included in the study after written informed consent had been obtained. The patients/participants provided their written informed consent to participate in this study.EF is a Ph.D. student and has done the work of the paper, while RV closely supervised the work while giving ideas, feedback, and guidance.RV has shares in Nemo Healthcare BV, The Netherlands. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper explores the public perception of economic measures implemented as a reaction to the COVID-19 pandemic in Poland in March–June 2020. A mixed-method approach was used to analyse big data coming from tweets and Facebook posts related to the mitigation measures to provide evidence for longitudinal trends, correlations, theme classification and perception. The online discussion oscillated around political and economic issues. The implementation of the anti-crisis measures triggered a barrage of criticism pointing out the shortcomings and ineffectiveness of the solutions. The revised relief legislation was accompanied by a wide-reaching informative campaign about the relief package, which decreased negative sentiment. The analysis also showed that with regard to online discussion about risk mitigation, social media users are more concerned about short-term economic and social effects rather than long-term effects of the pandemic. The findings have significant implications for the understanding of public sentiment related to the COVID-19 pandemic, economic attitudes and relief support implemented to fight the adverse effects of the pandemic. The COVID-19 pandemic is an unprecedented crisis that is having a major impact on state and international security. The fast spread of the virus has caused many hospital admissions and a rapidly increasing death toll. A near global lockdown was implemented and although this has been partially lifted since May 2020, the pandemic is likely to have far-reaching consequences, the eventual scale of which is impossible to predict at the moment (November 2020). Stay-at-home orders are being issued globally and social distancing is being promoted to reduce the risk of contracting the disease. Undoubtedly, the consequences of the pandemic surpass the impact on public health and health care systems and will have a massive impact on state and international security. Multiple policy interventions all over the world were launched to tackle the crisis, including imposing national lockdowns and closure of retail businesses. Various countries adopted different strategies to deal with the outbreak of the crisis (March–April 2020). Some countries, e.g. Italy, implemented a total lockdown, several other countries, e.g. the UK, reduced the functioning of businesses and services, whereas a few countries, e.g. Sweden, employed a relaxed strategy based on merely promoting social distancing without shutting down or reducing business operations. The short-term effects of socio-economic restrictions on the epidemiological situation have been partially effective , 4, an oSince there is little published data yet on the public perception of economic measures implemented to fight the adverse effects of the pandemic, this paper investigates communication on two social networking sites, Facebook and Twitter, to explore public opinion of the relief package launched in the face of the COVID-19 pandemic. The study seeks to answer the following research questions: How do social media users evaluate COVID-19 economic measures? What factors affect the perception of economic measures? We focus on social media discussion about the relief package introduced by the authorities in Poland at the outset of the pandemic (March–June 2020). We seek to make two contributions to the literature on this topic. Firstly, the study is important because research on the polarity of economic news during the COVID-19 related economic slowdown provides important clues both to the authorities (on how to improve legislation) and to journalists (on how to frame the news to prevent crisis development). Secondly, given that the framing of the crisis has a significant impact on economic and political performance, our findings are important. Negative economic attitudes make the financial market more susceptible to recession and slow down economic recovery. The consequences of lingering negative sentiment may activate “a mutually reinforcing cycle in which consumer behavior becomes detached from economic reality” . When ecRisk management is a systematic approach incorporated by every enterprise to manage uncertainty in order to satisfy stakeholders’ needs. The risk management process is crucial for developing company strategy and control. It is a dominant driver for value creation, competitiveness and profitability . EffectiThe pandemic has generated unprecedentedly high economic costs. In the face of the pandemic and recession, the whole world has implemented strict regulatory measures. Central banks, authorities and international organisations have launched numerous interventions to stimulate the economy . The IntAccording to the World Bank forecast, Poland is bound to experience a massive slowdown; however, the country has fiscal and monetary resources to mitigate the detrimental effects of decreasing global and domestic demand and protect the population at risk. Poland introduced a series of measures to mitigate the consequences of the economic crisis. On March 31, 2020, the Polish government launched new legislation , the so-. [. [. [. [A growing body of literature has been examining the impact of the pandemic on the global economy. Baker et al.  and Zhan. [.  analysed. [.  and Akht. [. [.  investig[. [. [.  investigSocial networking sites are a lens through which public sentiment can be easily accessed. A large body of research proves that social media data can be mined for a variety of reasons. Public sentiment displayed on social media can be used to predict the stock price of companies , highligWith the exponential growth of user-generated content published on social networking sites, a variety of actors  have turned to data mining. Social media mining includes an array of analyses, from simple counting of the likes, retweets and users’ demographics to more sophisticated measuring of quantifiable information such as sentiment, popularity or reach. Data mining techniques encompass social network analysis, Bayesian networks, decision trees, natural language processing, and other algorithms.Sentiment analysis examines the valence of social media data. The computation of the orientation of a post or tweet usually takes two main approaches: semantic orientation and machine learning. The semantic orientation approach is a rule-based linguistic model that extracts the sentiment from a text segment. This approach requires the creation of a dictionary of terms and their polarity value. The machine learning, on the other hand, is based on model creation by using annotated or weakly labeled data. Both methods produce relatively accurate polarity values. Dictionaries, such as SentiWordNet, are available in English; for other languages, the creation of individual sentiment lexicons is necessary. Furthermore, the context of the message, e.g. irony, is not taken into consideration. On the other hand, machine learning requires prior training; it also depends on the availability of labeled training datasets which are difficult to attain.Social media mining has become a popular method of research because social networking sites are the key communication platforms that are used universally for public debate –30, whic. [The abovementioned studies prove that the sentiment of social media users accurately predicts a range of behaviors. The pandemic resulted in increased public concern. COVID-19 risk perception has intensified due to several terrifying factors, such as the contagiousness of the infection, fast rising morbidity and mortality, and insufficient measures for infection prevention and control. In a study on risk perceptions of COVID-19, Dryhurst et al.  found th.  investig.  focused A large data set was investigated in order to determine public perception of economic measures implemented at the outset of the COVID-19 pandemic. The study aims to answer the following research questions: (1) How do social media users evaluate COVID-19 economic measures? (2) What factors affect the perception of economic measures? In order to answer the research questions, both quantitative analysis and content analysis were employed. Two variables were examined in order to determine the public perception of economic support offered by the government: the number of infected cases and online media coverage.We collected data from Facebook and Twitter, as these platforms are the most popular social networking sites in Poland that allow users to express their opinions on a variety of topics. Furthermore, relevant tweets and posts can be easily categorised through the use of hashtags and key words. We used Twitter Streaming API to collect tweets and Unamo social media mining software to harvest Facebook posts and news alerts published by media companies. All data was collected from public domains; therefore, it was not necessary to obtain user permission to conduct the analysis. Both metadata and textual features of mentions were collected. Metadata refers to the characteristics of the mention, such as user id and publication date. Textual features are the content of the tweet or post. The corpus contains 666,495 tweets and Facebook posts and 9,746 online news items harvested through a combination of hashtags and keywords associated with the impact of the pandemic in Poland in the period between 4 March and 24 May 2020. 4 March 2020 was the day when the first confirmed case of COVID-19 was reported in Poland. The hashtags through which the corpus was collected were actively used in the public debate on social networking sites. They referred to the economic effect of the pandemic and instruments implemented to mitigate these effects . Selecting mentions on the basis of hashtags offers an opportunity to gather the most relevant content related to COVID-19.Having collected the corpus, the next stage involved pre-processing and cleaning tweets and posts. This was an indispensable stage in creating a dataset because the presence of noise in a sample may result in inaccuracy. First, the data was filtered to remove redundant posts. The corpus had to be screened as it contained numerous items of marketing information or political content that was not directly related to the economic impact of the pandemic. Next, the data was cleaned using Python. Cleaning entailed removing the Twitter handle and Facebook username, lemmatization and tokenization as well as removing punctuation, stop words, special characters, and duplicate entries.Next, the corpus was analysed quantitatively. Descriptive statistics were used to calculate frequencies, means and standard deviations (SD). The linear regression was carried out to identify a correlation between the number of posts and two variables: the number of confirmed cases of COVID-19 reported in Poland and the number of news published online by media companies.A qualitative analysis was carried out on 33 325 tweets and posts (5% of the corpus). These entries were selected for the dataset if they contained at least one keyword from two prefixed lists of keywords . The post or tweet was therefore selected for qualitative analysis if it contained at least 2 keywords, which limited the size of the sample. The lists of keywords ensured the relevance of posts, i.e. all of them referred to the economic effects of the pandemic. The dataset was analysed with C# media analytic tool to identify dominant themes associated with the economic impact of the pandemic. The detection algorithm was able to find the key words in any part of the post or tweet.The sentiment analysis, i.e. the method used to examine the valence of social media data, was performed using lexicon-based analysis . This method gauges the sentiment of the words and phrases from the dataset based on their semantic orientation in a dictionary. Since the dataset was in Polish, we could not rely on available resources. Instead, we applied a three stage-based methodology that included manually constructing dictionaries, classifying data, and applying the prediction algorithm . In orde109,022 tweets and 557,473 Facebook posts related to the financial, business and economic effects of the pandemic were published during the study time frame, with an average 2,659 tweets and 6,798 posts per day. We reported a high degree of variation in the number of mentions depending on the publication date and channel   , posts pointing out the ineffectiveness of the implemented relief package 54.7%), posts indicating how effective and adequate the relief package was (7.3%), and posts identifying long term effects of the COVID-19 pandemic (0.7%). Figure .7%, postThe timeline clearly shows that the topic of the post corresponds to the relief package implemented by the government. At the outset of the pandemic, social media users posted comments demanding that the authorities introduce some risk mitigation measures that would counter the effects of the pandemic. When the first anti-crisis shield was implemented on March 31, citizens pointed out its shortcomings and ineffectiveness. The revised anti-crisis shield was launched on April 16 and May 14. At that time, informative posts prevailed as both the mainstream media and authorities launched a wide-reaching informative campaign about the relief package that was made available to companies, organisations, and individuals. The number of posts pointing out the effectiveness of the relief package rose immediately after the first anti-crisis shield was launched. These posts were also more frequently posted after the revised anti-crisis shield was implemented. The long term effects of the pandemic have not attracted much online attention. Table The theme classification model presents topics that prevailed throughout the time frame of the study relating to the negative effects of the pandemic on the economy with the focus on short-term effects and demanding the authorities take some action. When the news of the relief package came out, social media users started to point out the weakness of legal solutions and problems with administering the relief package related to the delay in processing the application and payment of benefits. These posts prevailed for a fortnight during the period when the first anti-crisis shield was launched. They drastically declined just before the third anti-crisis shield was launched. A large volume of informative posts and tweets was posted for a fortnight during the period when the second anti-crisis shield was launched. These posts not only offered a general explanation of the bill but also gave detailed advice regarding specific problems entrepreneurs and employees might have had. Posts referring to effective and adequate relief fell mainly into two groups: posts with hyperlinks to mainstream media highlighting the effectiveness of the relief launched in the bill or personal stories posted by social media users who had been the beneficiaries of the relief package. Finally, a few posts referred to the long term economic effects of the pandemic. Posts in this group mainly concerned high inflation and budget deficit, negative impact on investment and pension schemes being under strain. Table The obtained results suggest that the neutral sentiment prevailed. Based on the results shown in Figs. Further analysis was carried out to identify the most common terms in the corpus. Two strands of terms were identified: economy-related terms and words related to politics and government. Economy-related terms included “shield”, “work”, “economy”, “anti-crisis”, “loan”, “tax”, “bank”, “rights”, which in the context of the COVID-19 pandemic can be categorised as risk-related terms. As these words prevail in the corpus, it can be concluded that the public are apprehensive of the economic and social risk of the pandemic and discuss risk mitigation schemes. The respect for human and civil rights of citizens is important in the face of the pandemic. The widespread occurrence of words related to politics and government  is connected, on the one hand, with the government being responsible for passing several bills but also with casting blame on the government and politicians for the disastrous effect of the pandemic. On the other hand, the pandemic broke out on the eve of presidential elections , which affected the social media discussion.The findings of this study provide new insights into possible factors shaping economic attitudes. Contrary to other studies  which prThe study shows a weak correlation between the number of economy-related mentions and the coverage of this topic on online news sites. This finding shows the impact of the mainstream media coverage on discussion on social media platforms. First, mainstream news is reposted or retweeted, which affects the volume of the mentions. Next, the media decides the topics we discuss and this influences public opinion (the agenda-setting theory), which is in line with other studies –38. The The analysis of longitudinal trends and the theme classification model prove the association with economic trends related to the implementation of the relief package by the government. At the outset of the pandemic, social media users posted comments demanding the authorities introduce some risk mitigation measures that would counter the effects of the pandemic. The implementation of the anti-crisis shield on March 31, 2020 triggered a barrage of criticism highlighting the shortcomings and ineffectiveness of the proposed solutions. The revised relief legislation was launched on April 16 and May 14 accompanied by a wide-reaching informative campaign about the relief package. This led to a reduction in negative sentiment. The analysis also showed that as far as economy-related discussion on risk mitigation is concerned, social media users are concerned more about short-term than long-term effects of the pandemic. Long-term concerns were mainly related to high inflation and budget deficit, negative impact on investment and putting pension schemes under strain.This study set out to investigate the public perception of economic measures implemented to fight the adverse effects of the COVID-19 pandemic. Social networking sites were mined to examine change in longitudinal trends and specify events that affect those trends. This study revealed that the online debate on COVID-19 economic measures oscillated around political and economic issues. The implementation of the anti-crisis measures triggered a barrage of criticism pointing at the shortcomings and ineffectiveness of the solutions. The revised relief legislation was accompanied by a wide-reaching informative campaign about the relief package, which decreased negative sentiment. The research has also shown that social media users expect the implementation of measures to support the security of the state and to mitigate the negative economic and social effects of the pandemic but maintaining respect for the rights of the employee.The findings have significant implications for the understanding of public sentiment related to the pandemic, relief support and risk communication. Since there is little published data yet on the public perception of measures implemented to fight the adverse effects of the COVID-19 pandemic, this paper offers a significant contribution to the field of study. The research proves that social media helps to understand the public attitude , 40, 49.The major limitation of the study is that it has not provided clear evidence on the predictive value of tweets and posts. Furthermore, the distribution of sentiment analysis over time would provide accurate information on public opinion during different stages of the pandemic. More research is needed to further investigate the deeper meaning and correlations of the study results. A greater focus on regression analysis could produce interesting findings that account more for the explanation on how to utilise social media to promote global economic, health, and social objectives in the context of the pandemic.


Underwater wireless sensor networks are currently seeing broad research in various applications for human benefits. Large numbers of sensor nodes are being deployed in rivers and oceans to monitor the underwater environment. In the paper, we propose an energy-efficient clustering multi-hop routing protocol (EECMR) which can balance the energy consumption of these nodes and increase their network lifetime. The network area is divided into layers with regard to the depth level. The data sensed by the nodes are transmitted to a sink via a multi-hop routing path. The cluster head is selected according to the depth of the node and its residual energy. To transmit data from the node to the sink, the cluster head aggregates the data packet of all cluster members and then forwards them to the upper layer of the sink node. The simulation results show that EECMR is effective in terms of network lifetime and the nodes’ energy consumption. The ecosystem on Earth mainly consists of water, which covers more than 75% of its surface. Water covers the Earth in many forms as rivers, lakes, and oceans and plays a crucial role in human life and for other animals. Technological developments have allowed sensors to be deployed for the exploration of nature’s forest, river, and lake environments. These sensors are embedded with smart sensing and intelligent computing and are capable of communicating with each other. Underwater wireless sensor networks (UWSNs) consist of many autonomous sensor nodes, which are considered homogeneous nodes and limited in energy ,2. TheseIn both TWSNs and UWSNs, data packets are sent from a sensor node to the base station via either a one-hop path or a multi-hop path. It is necessary to route the data packet from one node to another node without loss. Since each sensor node is equipped with a battery, which may be difficult to replace or charge, the re-transmission of data packets may consume high energy, which results in a short network lifetime or many dead nodes. In TWSNs, many clustering protocols have been developed to minimize the energy consumption of sensors while successfully transmitting data packets to the destination. In TWSNs, all nodes are homogeneous and energy-constrained, and a low-energy adaptive clustering hierarchy (LEACH) successfully reduces the energy consumption of nodes while maintaining their network lifetime . In 7],,7], a clThe underwater sensor nodes update the routing path and cluster to connect to the sink without disruption. The sink collects data, which are then transmitted to an onshore gateway.Three types of node: cluster head (CH), cluster member (CM), and cluster relay (CR). If a node is the cluster head, the role of the node can be an aggregation node. If a node is the cluster relay, the node forwards data from two clusters at two depth levels. Otherwise, nodes are designated as a cluster member and only send data to the cluster head.The underwater sensors calculate the weight according to the node’s depth and residual energy to elect to become the cluster head.The routing protocol takes into account the number of cluster members and load balancing parameters so that the node selects the best route to the destination.In the present paper, we develop a clustering protocol with regard to the location of nodes and residual energy of nodes. We propose an energy-efficient clustering multi-hop routing protocol (EECMR) for UWSNs so that each node can maintain its transmission link to the sink node at the surface and its neighbor sensor nodes. Our contribution through this work is as follows:The remainder of the paper is organized as follows: UWSNs have attracted many researchers over the last decade because of their enormous variety of application. Many literature reviews of routing protocols for UWSNs categorizing the routing protocols in terms of the routing strategy have been presented . The rouIn , severalIn , a clustAnother study applied LEACH in UWSNs to establish a cluster . The stuIn , the autIn , networkIn , a topolIn , anotherIn , an enerIn , the autIn , autonomIn , a clustIn , the autThere remains some issues and challenges of clustering protocols in UWSNs. The type of nodes and the depth of nodes should be considered because the nodes may generate different types of data such as temperature, density, acidity, chemicals, conductivity, pH, and hydrogen which are categorized into different types of priority level. Therefore, nodes which generate high traffic priority should have higher priority and higher reliability to transmit data to the sink. In addition, the nodes located near the sink always aggregate data from other nodes which may cause a hot spot and dead nodes. Therefore, nodes should change the cluster head role after some time. In , the autN dynamic nodes which are randomly and sparsely deployed in a three-dimensional scenario L × L × L. The node knows its location and the location of the sink node upon first deployment.Nodes can become either the cluster head, cluster relay, or cluster member.The cluster head is rotated between the sensor nodes to conserve energy.The network scenario consists of L is the depth of network deployment and Tx is the transmission range of the node or sink. The depth of deployment can be divided into layers, where each layer is defined by the transmission range of the node or sink, as given in (1):D0; (2) nodes stay near the surface, whose depth is D1; (3) nodes stay in the water, whose depth is D2; (4) nodes stay near the seabed, whose depth is D3, where D3 > D2 > D1 > D0. The role of the nodes can be explained as follows:S3 is the cluster head at depth D1 and node S9 is the cluster head at depth D2.Each layer has several clusters, each cluster having one cluster head and a cluster member. For example, node S5 becomes a cluster relay which forwards the data of cluster S9 to cluster S3 and then transmits to the sink node.Nodes located at the border of two layers can adopt the role of cluster relay, which forwards the data of the deeper layer to the sink. For example, node S13 located at the seabed takes the multi-hop routing path to the sink via clusters of S12–S9–S5–S3–S1–sink. The propagation loss model of the underwater acoustic channel is assumed, as in 7], and EThe network scenario consists of dynamic nodes deployed sparsely in a three-dimensional environment. The water current causes node movements, which change the topology rapidly. The sink is located at the center of the surface, as shown in An example of a network after the implementation of EECRM is shown in In this work, the network lifetime is defined as the total time that the nodes are alive, which can be considered the number of rounds. When the residual energy of a node decreases to zero, the node is considered a dead node. In our simulation, we compared three protocols to evaluate the network lifetime while varying the number of nodes in 1000 rounds, as shown in In In In In In In a comparison of the residual energy performance with different transmission ranges in EECMR, the residual energy for a 200 m transmission distance was higher than a 150 m transmission range. This can be explained as follows. The cluster heads in EECMR aggregate the data of all cluster members and then forward these to the cluster relay which belongs to another cluster. Since the number of data packets is re-transmitted via multi-hop, the amount of energy consumption increases. In addition, the change in the network topology causes the re-established cluster to consume energy-transmitting and -receiving control packets. However, the total received packets at the sink should be considered. This is presented in the next section.We assume that all the sensed data of nodes will be received at the sink. In the network deployment, the network topology changes every 100 rounds, which may cause a failure to receive packets at the cluster head and cluster relay. This is because the distance from a cluster member to the cluster head is greater than the transmission range and the received packets at the cluster head or cluster member may fail as a result of a low level of received power. Therefore, the cluster must be re-established, and a new cluster head, cluster member, and cluster relay must be selected. In In In this work, we propose the energy protocol EECMR for routing data packets in UWSNs. EECMR is a depth-based clustering protocol that uses the depth level of the node to select cluster head nodes and forwarder nodes for multi-hop routing. EECMR considers the residual energy of the node which elects cluster heads in turns. The nodes can change roles as cluster head, cluster member, and cluster relay. The cluster relay node forwards data from a deeper level to the sink. With the aid of a cluster relay, the energy consumption for transmission is decreased, leading to fewer dead nodes. The simulation results showed that EECMR achieves better performance in terms of higher residual energy, longer network lifetime, and higher received packets at the sink. Although the proposed protocol can properly select the cluster head and cluster relay according to the depth level and residual energy, the high energy consumption at the cluster relay between different depth levels may result in re-clustering or frequent re-clustering. However, the clustering protocol may cause high latency due to the multi-hop routing path. The different types of sensor nodes will have different data priorities, an issue which must be addressed in future work. In addition, several issues remain open for future work, including optimization of the network topology and implementation of the clustering protocol in firmware to support UWSN application in oceans. In our future work, we will implement the clustering protocols into firmware to investigate the practical performance.


Emergency department (ED) overcrowding is a worldwide problem that poses a threat to patient safety by causing treatment delays and increasing mortality. Consultations are common and important in the emergency medicine profession and are associated with longer ED length of stay (LOS). The purpose of this study was to evaluate the impact of admission decisions by emergency physicians without consultations on the ED LOS and other quality indicators.  The study was a retrospective observational study comparing the ED LOS of patients admitted to the internal medicine (IM) department before and after the policy change regarding admission decisions that was implemented in October 2016. During and after the policy change, emergency physicians decided how to arrange for and treat medical patients by processing their admission and providing follow-up care without consultations. The ED LOS and other indicators of patients admitted to the IM department were compared between the study period (January to June 2017) and the control period (January to June 2016).  The median ED LOS of patients admitted to the IM department decreased from 673  minutes in the control period to 237 (IQR: 166–364) minutes in the study period. There were no significant differences in the interdepartmental transfer rate or in-hospital mortality between the two periods.  The admission decisions regarding medical patients made by emergency physicians without specialty consultations reduced the ED LOS without a significant negative effect on mortality or hospital LOS. Emergency department (ED) overcrowding is a worldwide problem that poses a threat to patient safety by causing treatment delays and increasing mortality –3. A proConsultations are common and important in the emergency department . ED consED consultations are performed to resolve issues regarding hospital admission, to obtain expert opinions, to provide treatments or procedures, and to obtain outpatient referrals ,13. AmonThe study was a retrospective observational study comparing the ED LOS of patients admitted to the IM department before and after the policy change regarding admission decisions that was implemented in October 2016. Before the policy change, consultations were mandatory for all patients requiring admission, as described in previous reports ,16. DuriThis study was approved by the Institutional Review Board (DAUHIRB-19-116). Written consent was waived by the committee because of the observational and noninvasive methodology of the study.The Dong-A University Hospital is located in Busan, a metropolitan city in the Republic of Korea. It is an academic tertiary-care hospital, and the annual ED census is approximately 32,000. The ED LOS and other indicators of patients admitted to the IM department were compared between the study period (January to June 2017) and the control period (January 2016 to June 2016). The study and control periods were selected to exclude the transition of practice that occurred at the end of 2016 and to avoid the effect of seasonal variation.The data that were extracted from the hospital information system database included the age, sex, ED LOS, admitted unit , admitted department and subspecialty, discharged department and division, hospital LOS, and the Korean Triage and Acuity Scale (KTAS) score from all the patients from the ED admitted to the IM department. KTAS is a modified assessment tool based on the Canadian Triage and Acuity Scale (CTAS) that was adapted for the Korean medical environment –23. WhenP values below 0.05 were considered statistically significant. All analyses were conducted using R, version 3.6.1 .The continuous data were presented as medians and interquartile ranges (IQRs) because the variables analyzed in the study, including age, appeared to have skewed distributions. The KTAS score was treated as an ordinal variable. The categorical data were summarized as frequencies (%). The continuous and ordinal data were compared using Wilcoxon's rank sum tests, and the categorical data were compared with chi-squared tests. P < 0.001, P < 0.001).A total of 15,326 patients visited the ED in the first half of 2016, and 15,031 visits were recorded during the same period in 2017. The characteristics of the general ED patients and ED workforce are summarized in This study revealed that terminating the mandatory consultation policy reduced the ED LOS of admitted patients and thereby the overall ED LOS.Howell et al. reported that admission decisions based on telephone consultations between ED physicians and in-house hospitalists rather than internal medicine residents reduced the ED LOS from 2.5 hours to 18 minutes . Our resP < 0.001), the medians and interquartile ranges were not considerably different between the periods. With a large sample size, statistical significance is likely to be present when there are no clinically significant differences; thus, in this study, the age and hospital LOS results should be interpreted accordingly [There may be some concerns about the possible negative effects of eliminating specialty consultations. However, the in-hospital mortality and rate of interdepartmental transfers were not significantly different between the two periods in this study. Although the differences in age and hospital LOS were statistically significantly different between the study and control periods  as a surrogate marker. The time to the first CPOE significantly decreased after the policy change (from 16.5 to 10.8 minutes), which can be explained by the decrease in ED overcrowding, positively influencing the timeliness of the ED. Moreover, while the burden of prolonged care for the patients increased, the burden of notifying and discussing issues with specialists in consultations decreased.This study has several limitations. Because it was a retrospective observational study in a single center in Korea, the extent to which the results can be generalized to other centers with different situations and policies may be limited. Additionally, other changes in the way in which the hospital operates or the decrease in the overall number of ED visits that occurred during the one-year study period might have influenced the results. However, the reduction in the ED LOS was much more substantial in the population admitted to the IM department than the other ED patients. We could not control the effect of the severity of the patient conditions on the LOS and mortality. Although we found no difference in the KTAS score of the admitted patients between the two periods, the KTAS and the CTAS are used as initial triage tools rather than prognostic tools in the ED . HoweverAdmission decisions regarding medical patients made by emergency physicians without specialty consultations reduced the ED LOS without a significant negative effect on mortality or hospital LOS.


It is necessary to periodically obtain topographic maps of the geographical and environmental characteristics of tidal flats to systemically manage and monitor them. Accurate digital elevation models (DEMs) of the tidal flats are produced while using ground control points (GCPs); however, it is both complicated and difficult to conduct GPS surveys and readings of image coordinates that correspond to these because tidal flat areas are not easy to access. The position and distribution of GCPs affect DEMs, because the entire working area cannot be covered during a survey. In this study, a least-squares height-difference (LHD) DEM matching method with a polynomial model is proposed to increase the number of DEM grids while using a presecured precise DEM to rectify the distortion and bowl effect produced by unmanned aerial vehicle (UAV) images. The most appropriate result was obtained when the translation parameters were quadratic curve polynomials with an increasing number of grids and the rotation parameters were constant. The experimental results indicated that the proposed method reduced the distortion and eliminated the error caused by the bowl effect while only using a reference DEM.  Coastal areas are vulnerable regions that are easily affected by natural and artificial environmental changes. There is growing interest in these areas in a number of associated fields, as various forms of land (sediment) have either emerged or disappeared, owing to these changes ,2; a uniIt is necessary to periodically obtain topographic maps that understand the geographical and environmental characteristics of tidal flats to systemically manage and monitor them. The bottom topography of an intertidal flat in a time series manner can be used, particularly to estimate the annual or seasonal variations in the sediment budget and coastal changes ,6,7. VerUntil now, the topography of tidal flats was obtained by using either airborne light detection and ranging (LiDAR) or echo sounders ,9. In 19http://www.ecosea.go.kr) [In recent times, a number of studies were conducted to assess the possibility of generation of the topography of tidal flats using unmanned aerial vehicle (UAV) images and photogrammetric software, such as Agisoft PhotoScan or Pix4D. According to these studies, on-site GCPs were required for the accurate development of digital elevation model (DEM) ,7,15. Hoa.go.kr) .2 containing buildings, roads, and meadows, the average DSM accuracy had a ground sampling distance that was obtained while using SenseFly eBee-RTK was approximately 3.7 without the GCPs [Without GCPs, the DEM generated from the UAV images and photogrammetric software probably have a nonlinear distortion that overestimated the elevation of the terrain. The systematic overvaluation of the terrain increased as the distance from the center increased, due to the ‘bowl or doming effect’ ,18, whicthe GCPs . Howeverthe GCPs . The basThis study proposes an least-squares height-difference (LHD) DEM matching technique with a polynomial model to correct the distortion in the DEM produced by the UAV, while using the existing reference DEM such as LiDAR or pre-compensated dense DEM instead of the GCPs. The LHD technique is based on the height difference in the same-plane positions between the two DEMs for the calculation of seven three-dimensional transformation parameters  ,23,24. HThe remainder of this paper is organized, as follows: The fundamentals of the proposed LHD matching are introduced in A UAV DEM built without GCPs usually contains a deformation, owing to the bowl effect. The bowl effect is probably affected by location errors in the tie-point that is used by the software for self-calibration and aerial triangulation. Therefore, a non-metric camera with inaccurate interior orientation parameters  is mounted on the UAV and the exterior orientation parameters  are determined by errors that are propagated by the bundle block adjustment process between overlapping images when a camera model with initially inaccurate values is used ,18,19.The purpose of our study was to rectify the DEM that is produced by the overlapping UAV images and photogrammetric software using LHD matching, because it is difficult to collect GCPs via field surveys in an inaccessible area, such as a tidal flat. We used only the ground coordinates of the pre-established reference DEM, in particular, to apply the LHD. The heights of the DEM were extracted by determining same-plane positions within a grid interval in both the reference and produced DEMs, as shown in Without the GCPs, a bowl-shaped deformation cannot be rectified, even by applying the LHD matching method while using the seven transform parameters and reference DEM . We propA seven-parameter 3-D similarity transformation was used to express the geometric relationship between the reference DEM and UAV DEM points:o indicates an initial value. Equation (1) can be linearized by Taylor series expansion, as shown in Equation (2), based on the theory of Rosenholm and Torlegård (1988):Equation (2) can be rewritten in terms of the Z-axis of the reference DEM is the same as that of the UAV DEM, then If the i = 1,2, …, n (n = number of points), and σ (σ is the standard deviation) of The test site chosen to apply the above-mentioned method was a tidal flat area in Hampyung Bay, which is located on the west coast of the Korean Peninsula . The sitCase 1: 7 parameters  from the acquired UAV image. The experimental results showed that the proposed method could reduce the error in the tidal flat DEM while using only the reference DEM and eliminated the bowl effect error. This method is very effective for correction in the DEM produced by UAV images obtained in inaccessible areas where GCP survey is difficult.The limitation of the method that is proposed in this paper is that the accuracy of the corrected DEM is affected by the accuracy of the reference DEM. As the height accuracy of LiDAR DEM used as the reference DEM is 15–20 cm, the UAV DEM can be regarded as having similar or lower accuracy, and it is necessary to use an independent reference point to evaluate the actual accuracy of the corrected UAV DEM.