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Color measurements have traditionally been linked to expensive and difficult to
handle equipment. The set of mathematical transformations that are needed to transfer a
color that we observe in any object that doesn’t emit its own light (which is usually called a
color-object) so that it can be displayed on a computer screen or printed on paper is n...
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Citations
... Most of the previous work found in the literature considered RGB-like sensors for modelling and predicting trichromatic responses and/or integral measures of lighting quality: Trinh et al. [26] demonstrated the possibility to estimate the circadian effectiveness of light sources in terms of the circadian stimulus (CS) metric from an RGB color sensor. Agudo et al. [27] developed a portable low-cost color-picking device for non-self-luminous surfaces by combining an RGB color sensor with an integrated high-power white LED. Botero el al. [28] proposed a method to estimate the correlated color temperature (CCT) from RGB sensor responses by using linear regression for the transformation from RGB responses to CIE XYZ tristimulus values together with McCamy's CCT approximation method [29]. ...
This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated ageing experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterisation of the sensor’s spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterisation of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017Δ
u
′
v
′ and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.
... Este espacio de color cuenta con 3 canales, L (Luminosidad), a (tonos rojos y verdes), b (tonos azules y amarillos), que permiten percibir los cambios de color de manera uniforme. CIE Lab facilita la detección de diferencias entre el verde y el rojo en el canal a sin verse afectado por los cambios de iluminación.El proceso de clasificación comienza con la imagen RGB original, con canales rojo R, verde G, y azul B, a la cual se le aplica la transformación al espacio de color CIE Figura 1. Extracción de los canales de color L, a y b de una imagen de entrada utilizando el espacio de color CIE Lab[8]. ...
El desarrollo de sistemas autónomos con aplicaciones en agricultura contribuyen de manera significativa a lograr una mayor eficiencia en las tareas diarias, como lo es el monitoreo de producción en el campo agrícola. En este trabajo se aborda precisamente este tema mediante la propuesta de un robot autónomo, que utiliza algoritmos de Deep Learning para detectar y clasificar fresas automáticamente según su grado de madurez. Con esta aplicación tecnológica se plantea proveer al agricultor información precisa de la cantidad de fresas presentes en un cultivo, así como su estado de madurez y localización exacta, lo que brinda al agricultor herramientas para planear una recolección eficiente. En escenarios reales de cultivo de fresas, existen factores que afectan la precisión en la detección de fresas, sin embargo, dados los resultados obtenidos en pruebas de campo, este sistema prueba ser preciso en esta tarea.
... Here, x, y, z represents the tristimulus systems and it is simulating the R. S basic three colors green, yellow and red in succession to reproduce the hue of a certain spectral power density (P). It is used to get phosphor's chromaticity coordinates of x and y [62]. ...
... Another application scenario of color sensing was considered by Agudo et al. [13]. Based on the Arduino architecture, they developed a portable low-cost color-picking device for the extraction and management of color information from any non-self-luminous object by using an RGB color sensor with an integrated high-power white LED. ...
Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu′v′ of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.
... The equation was utilized to measure the hue color angle value (Agudo et al. 2014;Vega-Gálvez et al. 2012). ...
... It gets lower values at pale colors and higher values at vivid colors. Chroma value (Kavdır et al., 2007) and Hue angle (Agudo et al., 2014) were calculated by using the following equations; ...
... The equation was utilized to measure the hue color angle value (Agudo et al. 2014;Vega-Gálvez et al. 2012). ...
To cite this article: İḃrahim Doymaz, Cüneyt Tunçkal & Zekiye Göksel (2023) Comparison of drying kinetics, energy efficiency and color of dried eggplant slices with two different configurations of a heat pump dryer, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45:1, 690-707, ABSTRACT In this study, Kemer eggplants (Solanum melongena L.) were examined in terms of system performance and energy efficiency after drying at constant air temperature and different operating systems developed on a heat pump dryer. The air velocity and the drying time have a negative correlation was observed. In addition, the Midilli & Kucuk model was the most appropriate model to explain the experimental data of dried eggplant samples obtained from this study. Effective moisture diffusivity was calculated between 6.08 × 10 −10 and 7.30 × 10 −10 m 2 /s depending on air velocity values. The reduction of the eggplant slices from 12.158 (g water/g dry matter (dm)) moisture value to 0.645 (g water/g dm) moisture value took 236 min at 1 m/s air velocity with the application of Serial Condenser and Parallel Evaporator Drying System (SCPEDS). The Serial Condenser Drying System (SCDS) was found to yield both the highest average condenser capacity with 2.256 kW and the highest system performance coefficient with 3.181. The brightness values (L) of dried eggplant samples increased compared to the fresh eggplant samples. The closest brightness value to the fresh samples was determined as 84.7 in the drying experiments with the air velocity of 0.5 m/s and the SCPEDS system. The other color parameters (b, C and h°) decreased during drying. The highest total color change value (8.05) was obtained with SCPEDS system at 1.0 m/s. The highest browning index value (63.48) was reached in the drying experiments performed with the SCDS system at 0.75 m/s. ARTICLE HISTORY
... Alternatively, smartphones, scanners or digital cameras were proposed as low-cost detectors for colorimetric assays performed by test strips or paper-based devices [2]. These tools return the color information by using a color space, i.e., a visualization that describes the color spectrum as a multidimensional model, including RGB (Red, Green and Blue), associated with smartphones screens and digital cameras [3]. Although attractive, these devices show some drawbacks, such as the sensitivity variation among different smartphones and the need for image processing by a computer or a mobile application to acquire RGB values, making real-time monitoring more time consuming [4][5][6]. ...
... The development of integrated low-cost digital platforms, for example, Arduino [7], Raspberry PI [8] or Mbed OS [9], is increasing for prototyping cheap instruments such as colorimetric detectors [3,[10][11][12][13]. Arduino is renowned for the prototype development of colorimetric detectors, thanks to its ease of use, simple programming, and reasonable price [3,[14][15][16][17][18][19][20][21][22][23][24][25]. ...
... The development of integrated low-cost digital platforms, for example, Arduino [7], Raspberry PI [8] or Mbed OS [9], is increasing for prototyping cheap instruments such as colorimetric detectors [3,[10][11][12][13]. Arduino is renowned for the prototype development of colorimetric detectors, thanks to its ease of use, simple programming, and reasonable price [3,[14][15][16][17][18][19][20][21][22][23][24][25]. ...
A user-friendly, low-cost detector able to read the RGB indexes of microfluidic paper-based analytical devices (µPADs) was developed. The RGB-detector was built with 3D printing using PLA+ and reused Li-ion batteries. It is Arduino-based, which provides an easy interface between the sensor TCS3200, which reads the quadratic wave of the times corresponding to the RGB numbers, the Arduino itself, whose software translates the times into RGB values, and the touchscreen display, NX3224T028, which shows the results. This detector permits multi-sample analysis since it has a sample holder that can keep up to six µPADs simultaneously and rotate after the display’s request. This work shows how the readings of the RGB indexes by the proposed RGB-detector implement the measurements’ reproducibility. As a proof-of-concept, the RGB-detector application to a green array of µPADs for pH measurement coupled with chemometric analysis allowed us to achieve good results in terms of precision and agreement with the pH values measured by a classical pH-meter.
... The closeness of the groups that the multivariate classification indicates gives the reason for the highest misclassification resulting when using the TCS3200 readings. The application goes beyond the knowledge that other authors have presented with similar colour recognition systems (Agudo et al. 2014) as the application of the system has been carried out in full field conditions. Plant nutritional status assessment with such a sensor is a practical, easy and low-cost operation for use both with single and large groups of plants. ...
In this work, basil plants were fertilized with 0, 2.5 mM and 10 mM nitrogen (with different NO3−/NH4+ ratios), and then monitored using a low-power technique based on an optical leaf meter and a low-cost RGB sensor interfaced with an Arduino UNO board. The study aimed to investigate possible relationships between the concentration of some plant compounds (i.e., leaf chlorophyll and flavonoids content) and the nitrogen balance index, with the output data of a low-cost RGB sensor to indicate its capability in discriminating among different levels of nutrition. The data obtained underwent univariate and multivariate analysis. The univariate data analysis showed that the low-cost RGB sensor readings followed the development of the plants according to the varying applications of nitrogen. The multivariate analysis of the data showed that the indices related to plant metabolic efficiency and leaf colour were those most affected by the nitrogen levels of the solutions used. The comparison of the discrimination powers of the systems showed that both systems achieved comparable discrimination performances (85.0% and 89.4%) for plants supplied with 0 mM nitrogen solution. However, at increasing levels of nitrogen, the RGB sensor performed worse than the optical leaf meter (− 15.8% and − 8.6% for the 2.5 and 10 mM N treatments). The effect of the NO3−/NH4+ ratio could hardly be distinguished (except for the total chlorophyll resulting from the optical leaf meter readings). More data is, however, necessary to create a more robust model for future implementation of the application of such a sensor.
... The precision and accuracy of the DPHP can be enhanced by the application of open hardware philosophy, which allows access to better and improved hardware design and their codes in parallel to "open source software" (Agudo et al., 2014;Fisher and Gould, 2012). In this direction, based on the philosophy of open hardware, improved, sustainable soil irrigation, and monitoring system was built (Masseroni et al., 2016) shown in Fig. 4. ...
Recent decades have witnessed unprecedented development in the field of biomedical and chemical science due to an economical, precise, and sensitive microelectromechanical system (MEMS) technology. Nevertheless, very few reports have highlighted the importance of MEMS-based sensors in the field of agriculture science and technology. Precision agriculture (PA) is a management strategy that employs advanced sensors marriage with information technology to improve productivity and quality of modern agriculture. This review unfolds the journey the conventional sensors have taken to come to the contemporary MEMS-based sensors. This review explains the fundamental principle of various sensors, presents outlines with a comparative study of sensors engaged in the field of agriculture. We have also elaborated on the importance of microcontroller addition in MEMS sensors to improve their sensitivity and productivity. Besides highlighting the pros and cons of the sensors, this review also brings a crisp discussion on the very recent sensors engaged to benefit agriculture and also takes into account the developmental aspects for commercialization.
