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A machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security. [ABSTRACT FROM AUTHOR]

The human body is a complex system, and the energy consumption in sports activities is influenced by many factors, making it challenging to accurately predict the degree of sports fatigue. Therefore, this article aims to use spectral sensors and machine learning algorithms to predict the level of sports fatigue among athletes. Spectral sensors capture biological signals inside the athlete's body and convert them into digital signals. Multiple optical parameters are selected as input features to reflect the physiological state and metabolic activities inside the athlete's body. After the data collection is completed, machine learning algorithms are used to analyze and process the data collected by the sensors, learn the patterns and patterns of the data, and make predictions based on these patterns and patterns. The results indicate that spectral sensors and machine learning algorithms can effectively predict the level of sports fatigue among athletes. The high correlation between the predicted results and the actual test results proves the reliability and accuracy of the proposed method. This method can provide timely and accurate fatigue monitoring for coaches and athletes, thereby helping to optimize training plans and avoid the occurrence of sports injuries. [ABSTRACT FROM AUTHOR]

With the continuous development of science and technology, virtual simulation technology is widely used in various fields and plays an important role in education. In the family physical education course, how to effectively use the virtual simulation platform to improve students' learning and physical education ability has become an important topic. By means of experiments and investigations, this study collected and analyzed the data of the virtual simulation platform of spectral sensor-based system used by students in the home physical education course. By understanding the needs and interests of students, the research designed a specific virtual simulation course, and formulated the corresponding experimental scheme based on the knowledge of optics. During the experiment, spectral sensors were used to collect data from the students and presented to the students through a virtual graphical interface. Through data analysis, the system virtual simulation platform based on spectral sensor provides visual experiment scenes, enabling students to understand optical principles and related concepts more intuitively, providing students with more operation and experiment opportunities, and promoting their participation and enthusiasm. The data collected through the virtual simulation platform can help teachers better understand the learning situation of students, and make timely adjustments and guidance. [ABSTRACT FROM AUTHOR]

In this article, we have designed both colorimetric (including solution and test paper type) and spectral sensors (including UV-vis and PL type) for the quick and sensitive detection of general nitrogen-containing organic bases (NCOBs); the limit of detection could reach as low as 0.50 nM. NCOBs included 11 examples, covering aliphatic and aromatic amines, five- and six-membered heterocyclics, fused-ring heterocyclics, amino acids, and antibiotics. Furthermore, the assays demonstrated high reliability in sensing NCOBs and excellent ability to distinguish NCOBs from oxygen and sulfur containing organics. The assays developed could find important applications for the detection of NCOBs in the fields of biomedicine, chemistry, and agriculture.

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.

We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity.

We report an active spectral sensor for chlorophyll detection based on quartz crystal tuning fork detector. The sensors use modulated diode laser radiations that can be differentiated from ambient background radiations by the detector. Experimentally, electrical and optical parameters of quartz crystal tuning fork detector (QCTF) were studied. Linear dependence of optical power to the generated electrical signals of QCTF detector was derived by measurements at different optical powers and wavelengths. We report here Quartz Enhanced Laser Photoacoustic signal of chlorophyll traces dissolved in water and adsorbed on surfaces, using 532 nm and 635 nm wavelength lasers from a standoff distance of up to 25 m. With the availability of high-power tunable diode laser, the technique offers a wide range of applicability for point and standoff sensing. [ABSTRACT FROM AUTHOR]

• A set of crop growth sensors mounted on a fixed-wing UAV were developed. The system can obtain crop canopies and growth indices, such as LNC and LAI, on-line and in a convenient, high-throughput manner while ensuring that theUAV's airflow does not disturb the crop canopies below. • Flight dynamics computer simulation analysis of the fixed-wing UAV was conducted by the ADAMS software to obtain the deflection angle of the UAV during flight. A ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. • Due to the adjustment of the sensor support, the system has good dynamic stability and measurement accuracy within the working height range of the sensor. The aim of this study is to overcome disturbance of downwash flow field caused by the low-altitude operation of a multirotor unmanned aerial vehicle (UAV) on crop canopies and interference in spectral reflection information of canopies. For this purpose, a crop growth sensor aboard a fixed-wing UAV was developed through flight dynamics simulation analysis of a fixed-wing UAV. This sensor can collect index data on-line and in real-time including: the ratio vegetation index (RVI) of crop leaves, leaf area index (LAI), leaf dry weight (LDW), and leaf nitrogen content (LNC). Flight dynamics simulation analysis of the fixed-wing UAV was conducted by the automatics dynamic analysis of mechanical system (ADAMS) software to obtain the deflection angle of the UAV during flight. According to the flight characteristics and load on the UAV, a ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. The field test results show that the crop growth sensor aboard the fixed-wing UAV has good dynamic stability and high measurement accuracy. The RVIs measured by the onboard crop growth sensor in the plots and field were fitted with the results measured by a FieldSpec HandHeld 2 spectroradiometer (ASD, Analytical Spectral Device Co., USA). By analysing the fitted results, the coefficients of determination (R 2) are 0.763 and 0.833 and the root mean square errors (RMSEs) are 0.16 and 0.17, respectively. By linearly fitting RVIs measured by the UAV with rice growth indices including LAI, LDW, and LNC, the coefficients of determination (R 2) are 0.633, 0.581, and 0.528 and RMSEs are 0.18, 0.18, and 0.21, respectively. [ABSTRACT FROM AUTHOR]

In this article, we have designed both colorimetric (including solution and test paper type) and spectral sensors (including UV-vis and PL type) for the quick and sensitive detection of general nitrogen-containing organic bases (NCOBs); the limit of detection could reach as low as 0.50 nM. NCOBs included 11 examples, covering aliphatic and aromatic amines, five- and six-membered heterocyclics, fused-ring heterocyclics, amino acids, and antibiotics. Furthermore, the assays demonstrated high reliability in sensing NCOBs and excellent ability to distinguish NCOBs from oxygen and sulfur containing organics. The assays developed could find important applications for the detection of NCOBs in the fields of biomedicine, chemistry, and agriculture. [ABSTRACT FROM AUTHOR]

This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from the plasma can be transferred through open-path optical configuration or via an optical fiber to a spectrometer to receive analytical information in the form of elemental composition. Thus, a relatively long-distance analysis can be performed using LIBS. Several modern experimental arrangements, patents and industrial notes are assessed, and the literature is reviewed. The review includes applications of LIBS to analyze steel, iron, aluminum, copper, slags, metal melts, and other materials. Temperature, pressure, and atmospheric composition are crucial parameters of any melting process. Hence, past studies on molten phases describing these parameters have been discussed. Finally, the review addresses the last technological advances for these types of applications. It also points out the need of development in some fields and some limitations to overcome. In addition, the review highlights the use of modern machine learning and data processing techniques to increase the effectiveness of calibration and quantification approaches. These developments are expected to improve the performance of LIBS systems already implemented at an industrial scale and ease the development of new applications in pyrometallurgical processes to address the stringent market and environmental regulations. [ABSTRACT FROM AUTHOR]

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor's field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors. [ABSTRACT FROM AUTHOR]