Red , Green And Blue Research Articles

A transformer-based image detection method for grassland situation of alpine meadows

As a vital role in climate regulation, water conservation, and maintenance of ecological balance, the alpine meadow grassland is facing the threat of degradation. Detecting grassland topography, phytomass, and grassland damage are important for improving the alpine meadow situation. This study reports a Transformer-CNN method for detecting alpine meadows situations using UnmannedAerial Vehicle (UAV) - based RGB (Red, Green, and Blue) data. This method combines Oriented FAST and Rotated BRIEF (ORB) and brute force feature matching to complete image stitching and then uses the proposed model Am-mask to complete the image segmentation task. The result shows that ORB feature matching is more stable and fast than SIFT and SURF for alpine meadow image stitching. In addition, Transformer has great application potential in grassland image detection and introducing task prefix and sparse in pre-training enhances the model’s robustness. The AP value of the Am-mask model with Transformer was as high as 95.4%, about 10% higher than that of the original CNN models. In the experiment with unstitched images, the average precision of the eight trials was 95.16%, the average recall was 95.13%, and the average F1 value was 95.14%. For stitched images, the average precision, recall, and F1 value of the eight trials were 91.83%, 91.81%, and 91.82%, respectively. It was proved that the proposed method could save the inference cost of the model under the condition of ensuring the detection effect. This study may contribute to grassland environmental protection in alpine meadows.

Prediction of significant oil properties using image processing based on RGB pixel intensity

Renewable oils from different feedstocks are considered a potential replacement for the current petroleum products, and they are widely used in various applications like alternative fuels to heat engines and bio-coolants for metal removal operations, and their analysis of significant physicochemical properties is significant before their application. This study attempts to analyze the significant oil properties of different edible and non-edible oils using an image processing technique. For this purpose, 11 different oils are considered for experimentation, and their surface images of all the oils are captured and utilized to extract significant pixel information of Red, Green and Blue (RGB). The RGB pixel information is correlated with experimental properties (viscosity, density, flash point, fire point, cloud and pour points) and the developed model is validated with KNN, SVM and Fiji techniques and observed a high prediction accuracy (99%, 90.9% and 99%). Finally, two different oils which are not used in the experiment are chosen to test the prediction accuracy of the developed model. The results show that the RGB pixel predicts the significant oil properties, and they are validated experimentally and observed that the developed model predicts the oil properties with 91.7% accuracy.

Sewage Pipe Waters Affect Colour Composition in Palaemon Shrimp from the Intertidal in the Canary Islands: A New Non-lethal Bioindicator of Anthropogenic Pollution

Marine pollution through anthropic outfalls like sewage pipes is a growing concern since point-source pollution can affect many organisms. Investigating pollutant concentrations in organisms usually requires sacrificing the organisms, but here we propose a new method to infer anthropic pollution in the intertidal by measuring colour levels in Palaemon elegans rockpool shrimp. We took pictures of live shrimp from pools near sewage pipes and control zones in three of the Canary Islands (Gran Canaria, Lanzarote and Tenerife), and measured their RGB (red, green and blue) abdominal colour composition. We then statistically compared colours from the control zone and sewage pipe and between islands. We found a clear differentiation in colour composition between the control zone and areas with a sewage pipe. Our results supported the hypothesis that pollution affects colouration in these invertebrates. We, therefore, suggest the use of darker colourations in P. elegans as a bioindicator of anthropic pollution, a first sign that should spur more in-depth studies in the affected area. This methodology is pollutant unspecific but non-extractive, so we propose its use as a citizen science tool to inform scientists and technicians of possible illegal and/or untreated wastewater that could affect intertidal biota.

Concurrent self-assembly of RGB microLEDs for next-generation displays.

MicroLED displays have been in the spotlight as the next-generation displays owing to their various advantages, including long lifetime and high brightness compared with organic light-emitting diode (OLED) displays. As a result, microLED technology1,2 is being commercialized for large-screen displays such as digital signage and active R&D programmes are being carried out for other applications, such as augmented reality3, flexible displays4 and biological imaging5. However, substantial obstacles in transfer technology, namely, high throughput, high yield and production scalability up to Generation 10+ (2,940 × 3,370 mm2) glass sizes, need to be overcome so that microLEDs can enter mainstream product markets and compete with liquid-crystal displays and OLED displays. Here we present a new transfer method based on fluidic self-assembly (FSA) technology, named magnetic-force-assisted dielectrophoretic self-assembly technology (MDSAT), which combines magnetic and dielectrophoresis (DEP) forces to achieve a simultaneous red, green and blue (RGB) LED transfer yield of 99.99% within 15 min. By embedding nickel, a ferromagnetic material, in the microLEDs, their movements were controlled by using magnets, and by applying localized DEP force centred around the receptor holes, these microLEDs were effectively captured and assembled in the receptor site. Furthermore, concurrent assembly of RGB LEDs were demonstrated through shape matching between microLEDs and receptors. Finally, a light-emitting panel was fabricated, showing damage-free transfer characteristics and uniform RGB electroluminescence emission, demonstrating our MDSAT method to be an excellent transfer technology candidate for high-volume production of mainstream commercial products.

Use of remotely piloted aircraft systems in monitoring pest and diseases in agricultural crops: global literature review

The use of Remotely Piloted Aircraft Systems (RPAS) as Remote Sensing platforms has been gaining more and more applicability in various fields of study, one of them being the agricultural sector. The interest in this technology segment is mainly due to its flexibility to acquire high-resolution data quickly. Data that helps, for example, identify and monitor pests and diseases. Therefore, this work aims to develop a systematic analysis at a global level to assess the evolution of publications over the years and the current state of the art in the use of RPAS technology in monitoring pests and diseases in crops. Twenty-nine scientific articles came from the Web of Science, Scopus, and Google Scholar platforms, identifying the places of origin of publications, agricultural species, platforms, and sensors. China and the United States published most of the works. Multirotor platforms have been more used compared to fixed-wing platforms. RGB (Red, Green, and Blue) and Multispectral sensors totaled 25% and 40.44%, respectively. It is expected that technological advances and RPAS improvement increasingly strengthened the control of pests and diseases in crops, contributing to a greater appreciation of the benefits of this system.

Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

AbstractFundus disorders like Glaucoma are a serious health risk that affects people all over the world, lowering their quality of life. Optic disc (OD) segmentation is a crucial step for automatic detection of glaucoma with fundus images. This paper presents an efficient and accurate OD segmentation methodology from eye fundus images. In the proposed methodology, fundus images are transformed from RGB (Red, Green, and Blue) color space to LAB (Luminance, A‐axis, B‐axis) color space. A new image is generated using the luminance, A‐axis component, and image information content. As a pre‐segmentation stage, the unsupervised approach extracts the region of interest containing OD. The adaptive threshold method, followed by morphological operations, extracts the contours of the OD region and removes the spur. Segmentation of OD is a challenging task due to similar intensity levels with neighborhood, vascular occlusion, and retinal atrophy. The proposed work overcomes the challenges by segmentation of the OD at three levels, which enhances the functionality at different illumination conditions. The features are extracted at various levels to determine whether the segmented image contains the OD or not. The efficiency of the proposed methodology demonstrates by applying it to a variety of fundus images from datasets, namely ISBI IDRiD, DRISHTI‐GS, and SYSU. Key performance metrics like Intersection over Union and Dice Coefficient are used to evaluate the performance of the proposed methodology. The proposed methodology achieves Intersection over Union of 0.996, 0.997, and 0.994 on ISBI IDRiD, DRISHTI‐GS, and SYSU datasets, respectively. Similarly, the proposed methodology obtains 0.96, 0.97, and 0.98 Dice Coefficient on the three datasets.

A multi-behavior recognition method for macaques based on improved SlowFast network

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.

High‐efficiency nanowire light‐emitting diodes for augmented reality and virtual reality displays

AbstractThrough 3D dipole cloud model, RGB (red, green, and blue) nanowire light‐emitting diode (LED) structures are geometrically optimized to exhibit a narrower radiation pattern, weaker angular color shift, and higher optical efficiency. In comparison with micro‐LEDs whose external quantum efficiency is dependent on the mesa size, these RGB nanowire LEDs are more efficient than micro‐LEDs when the chip size is below 20, 80, and 10 μm, respectively. As a result, nanowire LED is a promising candidate for augmented reality and virtual reality displays where high‐resolution density and efficient directional light emission are desired.

Thin wire segmentation and reconstruction based on a novel image overlap-partitioning and stitching algorithm in apple fruiting wall architecture for robotic picking

The layout of orchards usually requires the use of wires, to provide sturdy support. Such is the case of apple trees in fruiting wall architecture, where wires are conducive for mechanical harvesting and especially robotic picking. However, wires may cause damage to the robotic gripper, especially when direct picking occluded apple fruits. Hence, the importance of identifying the wires of a fruit wall architecture. In this study, a pixel-level segmentation network, BlendMask, was adopted to segment wires. The wires are thin and normally behind the branches or leaves, making difficult for their identification. Therefore, a novel data processing algorithm called image overlap-partitioning and stitching (IOS) is proposed for BlendMask to segment the wires. A total of 82 RGB (Red, Green, and Blue) images registered to create a raw dataset. The dataset was augmented and then overlap-partitioned into 12,736 images with a resolution of 800 × 1024 pixels and corresponding annotation files based on input size of BlendMask to make an overlap-partitioned dataset. Then BlendMask was trained with the overlap-partitioned dataset and tested on the other images, where additional stitching for the overlap-partitioned images was needed in the image testing dataset. Results showed that BlendMask with IOS obtained Intersection over Union (IoU) and pixel accuracy of 43.86 % and 61.01 %, respectively. It achieved a better average precision of 38.75 % with IoU of 0.5 on the overlap-partitioned dataset, which was 38.42 % higher than the full image dataset. Moreover, a reconstruction method based on feature point extraction and fitting was proposed to estimate wire skeletons, which achieved a reconstruction accuracy of 90.70 %. These results showed a promising potential using segmentation and reconstruction methods for identifying wires and thus providing a basis for robotic picking in modern orchards.

Colorimetric parameters for bloodstain characterization by smartphone

This smartphone colorimetry investigated RGB (red, green and blue), HSV (hue, saturation and value of brightness) and CMYK (cyan, magenta, yellow and key) color parameters for characterizing bloodstains. Blood samples, separately deposited on cotton cloths and tissue papers, were analyzed by the ‘Colorimeter X’ application on an iPhone XR after 1, 5, 7, 9 and 11 days. The reductions in RGB and V values recorded by the smartphone application are consistent with darker bloodstain over time. Bloodstain colors are sensitive to the substrate and person-to-person variation, but a similar correlation with time could be established by a linear least squares fitting. With the highest coefficient of determination averaged from four samples, the V value is the most effective parameter for describing bloodstain ageing. For other color spaces, the R and K almost match the V value in measuring the bloodstains. Because these three values may be susceptible to the deviation in the measurement, the B value is suggested as an additional parameter in the bloodstain characterization. This small-scale study demonstrates the potential of colorimetric parameters in an on-site analysis by a cost-effective smartphone.

Visible light photocatalytic activity in BiFeO3 glass-ceramics

Bismuth Ferrite (BiFeO3) being a visible light active ferroelectric material is a potential candidate for removing industrial dyes from water through the photocatalysis process. To take advantage of the BiFeO3 phase, glass-ceramic with composition Bi2O3–Fe2O3–BaO–B2O3 was fabricated through a conventional melt and quench process. The desired bismuth ferrite (BiFeO3) phase was achieved by optimizing heat treatment temperature (480° C) duration for 2, 4, and 12 h. Each fabricated sample was then utilized for dye cleaning application via photocatalysis process with visible light as an exciting source, where the sample heat-treated at 480 °C for 2 h has shown the maximum Methylene Blue (MB) dye degradation of ∼69% in 300 min. Further, the stability of the fabricated samples was checked through their XRD pattern and SEM micrographs taken before and after the photocatalysis process. At last, MB dye degradation was also calculated through a colorimetric response of MB dye with RGB (red, green, and blue) values obtained by an app of a smartphone.

A novel approach based on RGB image analysis for the investigation of scale formation and performance of inhibitors under flow conditions

Scale is one of the main flow assurance issues in oil and gas production because reduces productivity or even stops production. The evaluation of scale formation and performance of inhibitors under flow conditions is essential for managing this problem. A conventional method for this evaluation is the tube-blocking test performed in a dynamic scale loop (DSL) system monitoring the differential pressure caused by the scale formation. However, this system does not provide information to understand the precipitation process prior to scaling and the inhibition mechanisms. This work presents a novel approach based on RGB (red, green, and blue) image analysis for the investigation of scale formation and performance of inhibitors under flow conditions. This approach uses a viewing cell coupled to the output of a DSL that allows detecting the inorganic salt precipitation and formation of its deposits under flow conditions via RGB image analysis. Several calcium carbonate scale experiments were conducted by mixing different concentrations of bicarbonate ions (1,000–4,000 mg L−1) with calcium ions (4,000 mg L−1) and controlling the temperature (80 °C), flow rate (10.0 mL min−1), ionic strength (19,980 and 40,000 mg L−1 of Cl−), and pH (6.8). The results via RGB image analysis agreed with those provided by the differential pressure, including the evaluation of the performance and minimum inhibitory concentration of three chemicals: EDTA and two commercial inhibitors. This novel approach not only allows the determination of the scale formation time as the DSL but also allows the study of crystallization and crystals growth. Furthermore, it provides information helpful to better understanding the inhibition mechanisms.

Development and testing of a system for controlled ultrasound hyperthermia treatment with a phantom device.

Hyperthermia is the process of raising tissue temperatures in the range 40 - 45 °C for a prolonged time (up to hours). Unlike in ablation therapy, raising the temperature to such levels does not cause necrosis of the tissue but has been postulated to sensitize the tissue for radiotherapy. The ability to maintain a certain temperature in a target region is key to a hyperthermia delivery system. The aim of this work was to design and characterize a heat delivery system for ultrasound hyperthermia able to generate a uniform power deposition pattern in the target region with a closed-loop control which would maintain the defined temperature over a defined period. The hyperthermia delivery system presented herein is a flexible design with the ability to strictly control the induced temperature rise with a feedback loop. The system can be reproduced elsewhere with relative ease and is adaptable for various tumor sizes/locations and for other temperature elevation applications, such as ablation therapy. The system was fully characterized and tested on a newly-designed custom-built phantom with controlled acoustic and thermal properties and containing embedded thermocouples. Additionally, a layer of thermochromic material was fixed above the thermocouples and the recorded temperature increase was compared to the RGB (red, green, and blue) color-change in the material. The transducer characterization allowed for input voltage to output power curves to be generated, thus allowing for comparison of power deposition to temperature increase in the phantom. Additionally, the transducer characterization generated a field map of the symmetric field. The system was capable of increasing the temperature of the target area by 6 °C above body temperature and maintain the temperature to within ±0.5 °C over a defined period. The increase in temperature correlated with the RGB image analysis of the thermochromic material. The results of this work have the potential to contribute towards increasing confidence in the delivery of hyperthermia treatment to superficial tumors. The developed system could potentially be used for phantom or small animal proof-of-principle studies. The developed phantom test device may be used for testing other hyperthermia systems.

Investigation into the development of novel lanthanide‐based luminescent colorants for application to textiles and paper materials

AbstractThis article describes the synthesis and application of lanthanide‐based luminophores for visualisation under short wavelength ultraviolet (UV) activation (typically 254 nm). Luminophores are chemical compounds which re‐emit light of longer wavelength than typical fluorescence and phosphorescence, following photoexcitation, because of efficient energy transfer from the ligands to the lanthanide ion emission levels. The luminophores described in this article are all derived from europium and terbium metal complexes and cover the complete colour gamut. Selection of appropriate ligands allows for water solubility or water insolubility (if required pigment or disperse “dye” applications). Similar to dyeing or printing processes, the anionic complexes can be applied to polyamide fibres or to cellulosic fibres, whereas dispersions of the non‐ionic complexes are suitable for polyester or cellulose acetate fibre application. The water‐soluble derivatives are also suitable for ink‐jet printing and the water‐insoluble derivatives are suitable for lithographic or intaglio paper printing. The novel complexes are excited by radiation below 300 nm and thus cannot be activated by sunlight (the earth's atmosphere prevents light below about 300 nm reaching the surface). Accordingly, the photo‐stability of the dyed materials and prints obtained is excellent. These materials could be used in security marking applications, for example bank‐notes and passports, where they could replace the current fluorophores that are rendered visible under near UV light (typically 365 nm) illumination but exhibit relatively poor photo‐stability. The development of an RGB (red, green and blue) colour palette for inkjet printing based on these innovative luminophores will significantly support the design of digitally printed security features.

SunMap: Towards Unattended Maintenance of Photovoltaic Plants Using Drone Photogrammetry

Global awareness of environmental issues has boosted interest in renewable energy resources, among which solar energy is one of the most attractive renewable sources. The massive growth of PV plants, both in number and size, has motivated the development of new approaches for their inspection and monitoring. In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common faults (hot spots) in photovoltaic (PV) plants. The latest advances in photogrammetry and computer vision (i.e., Structure from Motion (SfM) and multiview stereo (MVS)), together with advanced and robust analysis of IRT images, are the main elements of the proposed methodology. We developed an in-house software application, SunMap, that allows automatic, accurate, and reliable detection of hot spots on PV panels. Along with the identification and geolocation of malfunctioning PV panels, SunMap provides high-quality cartographic products by means of 3D models and true orthophotos that provide additional support for maintenance operations. Validation of SunMap was performed in two different PV plants located in Spain, generating positive results in the detection and geolocation of anomalies with an error incidence lower than 15% as validated by the manufacturer’s standard electrical tests.

Vertical full-colour micro-LEDs via 2D materials-based layer transfer

Micro-LEDs (µLEDs) have been explored for augmented and virtual reality display applications that require extremely high pixels per inch and luminance1,2. However, conventional manufacturing processes based on the lateral assembly of red, green and blue (RGB) µLEDs have limitations in enhancing pixel density3-6. Recent demonstrations of vertical µLED displays have attempted to address this issue by stacking freestanding RGB LED membranes and fabricating top-down7-14, but minimization of the lateral dimensions of stacked µLEDs has been difficult. Here we report full-colour, vertically stacked µLEDs that achieve, to our knowledge, the highest array density (5,100 pixels per inch) and the smallest size (4 µm) reported to date. This is enabled by a two-dimensional materials-based layer transfer technique15-18 that allows the growth of RGB LEDs of near-submicron thickness on two-dimensional material-coated substrates via remote or van der Waals epitaxy, mechanical release and stacking of LEDs, followed by top-down fabrication. The smallest-ever stack height of around 9 µm is the key enabler for record high µLED array density. We also demonstrate vertical integration of blue µLEDs with silicon membrane transistors for active matrix operation. These results establish routes to creating full-colour µLED displays for augmented and virtual reality, whilealso offering a generalizable platform for broader classes of three-dimensional integrated devices.

Biofloc colors as an assessment tool for water quality in shrimp farming with BFT systems

In shrimp farming using biofloc technology it is common to observe a gradual swap in the biofloc colors from green to brown along with shifts in water quality throughout the culture cycle. This change reflects a dominance shift from an autotrophic to a heterotrophic microbial community, resulting mainly from changes in system C:N and N:P ratios. Notwithstanding the ease of the direct analysis of biofloc color, no assessment of its potential as a proxy of water quality has been done so far. Therefore, our aim was to develop a standardized protocol to determine the potential of using the microbial community color index (MCCI) to develop an easy, fast, and low-cost assessment of water quality of intensive marine shrimp farming using biofloc systems. In this study, water quality data were collected from 17 tanks and the biofloc color was obtained from images after filtering with the help of an apparatus built with materials accessible to shrimp producers. We developed and standardized a methodology for reading the average RGB (Red, Green, and Blue) values from pixels of bioflocs images and used this methodology to calculate their average MCCIs. Based on this quantification of colors, it was possible to calculate a correlation matrix among MCCI and several water quality variables. The MCCI was positively correlated with the concentration of carbon, nitrogen, and phosphorus concentration in the bioflocs, total suspended solids, settleable solids and total phosphorus and negatively correlated with water transparency and G (green) values. The MCCI association with important water quality variables was expected for this type of system, in which the dominance by heterotrophic bacteria occurs at the final stages of biofloc succession, during which the system undergoes nutrient accumulation, shifting the bioflocs color from green to brown shades. Thus, it was observed that the water quality variables correlated with the MCCI were those that varied according to the culture stage. Another important result was that artificial images generated from images’ average RGB yielded satisfactory results regarding the similarity to the original images. Therefore, since it is possible to estimate water quality variables from the biofloc colors, this method lays the foundation for the future development of a practical tool (color scale or mobile app) for water quality evaluation. Shrimp farmers can use it to conduct a cheap and fast assessment of water quality of their tanks and take better decisions about critical aspects such as fertilization, water renewal or mechanical removal of excessive concentration of bioflocs.

Full-color eye-box expansion via holographic volume gratings recorded in photo-thermo-refractive glass.

Conventional head-up displays (HUDs) suffer from a limited exit pupil and a lack of compactness mainly due to the use of bulky optics. HUDs need a high-quality image with a large field of view (FOV) in small packaging to gain commercial acceptability. Holographic HUDs are phase-only devices that allow vision correction and focus adjustment while having a wide FOV. However, the limited bandwidth of a spatial light modulator (SLM) imposes a trade-off between the FOV and eye-box size. Combining a holographic system with an image-replicating element eliminates such a tradeoff. For image replication, we designed and fabricated a compact 2D diffractive beam splitter formed from two perpendicular volume gratings operating in the Raman-Nath regime. The gratings were recorded holographically in photo-thermo-refractive (PTR) glass, with optimized index modulation, thickness, and period to provide uniform intensity distribution across all desired orders for the fundamental red, green and blue (RGB) colors. We demonstrated a full-color holographic projection with an eye-box expanded by the designed 2D diffractive beam splitters.

Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

For enhanced adaptability to poor light enhancement whilst achieving high image contrast, a new method for color image correction based on the advantages of non-linear function in grey transformation and histogram equalization techniques is proposed in this work. Firstly, the original red, green and blue (RGB) image is converted into the HSV color space, and the V channel is used for enhancement. An adaptive gamma generator is proposed to adaptively calculate gamma parameters in accordance with dark, medium, or bright image conditions. The computed gamma parameters are used to propose a cumulative distribution function that produces an optimized curve for illumination values. Next, a second modified equalization is performed to evenly correct the offset of the illumination curve values on the basis of the equal probability of the available values only. Finally, the processed V channel replaces the original V channel, and the new HSV model returns to the RGB color space. Experiments show that the proposed method can significantly improve the low contrast and poor illumination of the color image whilst preserving the color and details of the original image. Results from benchmark data sets and measurements indicate that the proposed method outperforms other state-of-the-art methods.

The spectral composition of a white light influences its attractiveness to Culex pipiens mosquitoes.

Insect attraction to artificial light can potentially facilitate disease transmission by increasing contact between humans and vectors. Previous research has identified specific wavelength bands, such as yellow and red, that are unattractive to biting flies. However, narrow-band, non-white lights are unsuitable for home lighting use as their very poor color rendering is often considered aesthetically undesirable. The creation of a white light that is unattractive to insects has so far remained elusive. White light can be created by combining a number of narrow-band light-emitting diodes (LEDs). Through choice chamber experiments on Culex pipiens (Cx.pipiens) mosquitoes, we examine whether combining specific wavelength bands has an additive, subtractive or synergistic effect on insect attraction. We show that a white light created by combining narrow-band red, green and blue (RGB) LEDs is less attractive to Cx.pipiens than a broad-spectrum white light; and that a white light created by combining narrow-band blue and yellow LEDs is more attractive than a broad-spectrum white light. White light produced by RGB combinations could therefore serve as a safer and cheaper light in countries where phototactic vectors and vector-borne disease are endemic.