Color Sensor Research Articles

Detailed characterization of iron-rich tailings after the Fundão dam failure, Brazil, with inclusion of proximal sensors data, as a secure basis for environmental and agricultural restoration

Following the Fundão dam failure in Brazil, 60 million m3 of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R2 and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fundão dam failure can be in detail characterized via pXRF ex situ, providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes.

Colour Detection using Arduino

Poor ocular understanding is the condition popular as sightlessness, that influences an supposed285 heap things everywhere, of whom 39 heap are blind and 246 heap have reduced apparition .The right preparation, a blind human can act tasks in addition to dignitary accompanying standard vision, but skilled are illuminated the belongings that can embellish a blind human's characteristic of growth, in the way that color labeling, that is the project's main objective .Color idea is beneficial for a assortment of tasks, containing seeing attire banner, recognizing belongings accompanying unique hues (like paper currency), and having skill. By cultivating a form to perceive banner and contribution particular biofeedback each color, the action will assist those the one are blind or optically injured in defeating regularly impediments. The submitted entrenched structure uses an Arduino microcontroller to express RGB dossier from aTCS230 color sensor, that is therefore treated to further types the color utilizing a lookup table that has happened set up into the boss. The color that has happened acknowledged is proved in addition to the RGB merger, and a OF performer related to an Arduino supplies hearing response of the color. 93% of attempts to recognize banner favorably all along experiment of the entrenched order.

Portable and Battery-Operated Isothermal Amplification Device Validation for Onsite Analysis of M. tuberculosis “DNA Hunter”

Point-of-care (POC) devices play an important role in the protection of public health by providing rapid diagnosis of infectious diseases, patient management, and effective treatment. Fast, easy-to-interpret, environmentally resistant, and cost-effective POC tests that can be used practically in the field are gaining more and more importance every day. There is a need for portable devices that will enable rapid diagnosis kits to be used in the field for early diagnosis and treatment. The aim of this study is to evaluate the DNA hunter device that was developed in terms of providing the required temperature for M. tuberculosis (MTB) diagnosis of the loop-mediated isothermal amplification (LAMP) assay and visually evaluating the analysis results. The device in this study; handheld (total weight 430 g, outer dimensions 70 x 175 x 80 mm), the average operating time can reach a maximum temperature of 110 degrees in 2 minutes with a fully charged battery, and the processing time is about 90 minutes without being connected to electricity. It can display the pre-evaluation result on the screen with the full digital color sensor. The device can be adjusted to the desired reaction temperature and time. It also has software where sample registration numbers can be entered. DNA Hunter can be used for all analyses performed by the LAMP method and the results can be evaluated colorimetrically, thus it is well suited for POC testing.

Development of a flexible high-friction pressure sensor with structural colors for soft gripper fingers

For energy-efficient and lightweight sensor applications, structural colors were utilized to sense the pressure applied to the surface. However, conventional structural color sensors have been usually fabricated with nanoscale features focusing on high pressure sensitivity with small load limits. In this study, a flexible surface with high frictional force and wide pressure-sensing capability was fabricated by ultra-precision machining for soft gripper applications. Friction and grip detection are very important for gripping performance and sensors for soft grippers are required to cope with loads of over 1 kg. Here, microscale three-dimensional trapezoidal patterns were fabricated and transferred to a polymer to increase the contact area for higher friction and improve structural color expression. The effects of pattern geometry on structural color changes due to the varying loads were analyzed using the hue, saturation, and brightness (HSB) model. The hues of the patterned surfaces decreased with loads. Clear color changes were apparent as the pattern width and depth varied; patterns with blue colors (hue of ~240) at no-load status sensed pressure most effectively. Based on the experimental results, a sensor was designed to have a clear color change in terms of pressure and then was applied to soft gripper fingers. Significant color changes were evident as the pressure changed during gripping even under large loads of up to 1200 g; the high friction (compared to that of a plain surface) also improved gripping performances. The sensor provides intuitive information detectable by the naked eye, and there is no need for any power source, wire, or data acquisition system. This research can contribute to the development of multi-functional, lightweight energy-efficient systems.

True-Color Imagery from GOES—A Synopsis of Past and Present

The human eye is sensitive to three primary bands of light—centered on the red, green, and blue parts of the visible spectrum. The human eye is not very sensitive to variations in shades of gray—being able to distinguish only approximately 25 different gradations of gray in satellite images. However, by using the three different color sensors, the eye has the potential to distinguish up to a million different values of color. Hence, color is a powerful tool for distinguishing various objects of interest with subtle intensity variations. The Geostationary Operational Environmental Satellites-R (GOES-R) series of geostationary satellites do not have a green channel. However, a synthetic green channel can be constructed from the blue, red, and nearinfrared “veggie” channels for the use in a true-color visible image. Since the launch of the GOES-16 satellite, several different groups have developed color visible algorithms that are available on public websites. The purpose of this paper is to help explain the similarities and differences of true-color GOES images that are on the web and in other locations.

Conceptual Design of a Device for Online Calibration of Spirometer Based on Neural Network.

Daily calibration of spirometry devices plays an important role in promoting the accuracy of pulmonary diagnostic results. It is needed to have more precise and adequate instruments for calibrating spirometry during the clinical use. In this work, a device was designed and developed based on a calibrated-volume syringe and an electrical circuit was also built to measure the air flux. Some colored tapes with specific size and order covered the syringe piston. When the piston moved in front of the color sensor, the input air flow was calculated according to the width of the strips and transferred to the computer. A Radial Basis Function (RBF) neural network estimator used new data to modify the previous estimation function for increasing the accuracy and the reliability. The simulation showed that the root mean square of the error improved from 13.7±0.37% to 4.2±0.22%, i.e. the calibration curve has improved about 70%.

Determining the primary sources of uncertainty in the retrieval of marine remote sensing reflectance from satellite ocean color sensors II. Sentinel 3 OLCI sensors

Uncertainties in remote sensing reflectance Rrs for the Ocean Color sensors strongly affect the quality of the retrieval of concentrations of chlorophyll-a and water properties. By comparison of data from SNPP VIIRS and several AERONET-OC stations and MOBY, it was recently shown that the main uncertainties come from the Rayleigh-type spectral component (Gilerson et al., 2022), which was associated with small variability in the Rayleigh optical thickness in the atmosphere and/or its calculation. In addition, water variability spectra proportional to Rrs were found to play a significant role in coastal waters, while other components including radiances from aerosols and glint were small. This work expands on the previous study, following a similar procedure and applying the same model for the characterization of uncertainties to the Sentinel-3A and B OLCI sensors. It is shown that the primary sources of uncertainties are the same as for VIIRS, i.e., dominated by the Rayleigh-type component, with the total uncertainties for OLCI sensors typically higher in coastal areas than for VIIRS.

IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture

Healthy and sufficient crop and food production are very much essential for everyone as the population is increasing globally. The production of crops affects the economy of a country to a great extent. In agriculture, observing the soil, weather, and water availability and, based on these factors, selecting an appropriate crop, finding the availability of seeds, analysing crop demand in the market, and having knowledge of crop cultivation are important. At present, many advancements have been made in recent times, starting from crop selection to crop cutting. Mainly, the roles of the Internet of Things, cloud computing, and machine learning tools help a farmer to analyse and make better decisions in each stage of cultivation. Once suitable crop seeds are chosen, the farmer shall proceed with seeding, monitoring crop growth, disease detection, finding the ripening stage of the crop, and then crop cutting. The main objective is to provide a continuous support system to a farmer so that he can obtain regular inputs about his field and crop. Additionally, he should be able to make proper decisions at each stage of farming. Artificial intelligence, machine learning, the cloud, sensors, and other automated devices shall be included in the decision support system so that it will provide the right information within a short time span. By using the support system, a farmer will be able to take decisive measures without fully depending on the local agriculture offices. We have proposed an IoT-enabled soil nutrient classification and crop recommendation (IoTSNA-CR) model to recommend crops. The model helps to minimise the use of fertilisers in soil so as to maximise productivity. The proposed model consists of phases, such as data collection using IoT sensors from cultivation lands, storing this real-time data into cloud memory services, accessing this cloud data using an Android application, and then pre-processing and periodic analysis of it using different learning techniques. A sensory system was prepared with optimised cost that contains different sensors, such as a soil temperature sensor, a soil moisture sensor, a water level indicator, a pH sensor, a GPS sensor, and a colour sensor, along with an Arduino UNO board. This sensory system allowed us to collect moisture, temperature, water level, soil NPK colour values, date, time, longitude, and latitude. The studies have revealed that the Agrinex NPK soil testing tablets should be applied to a soil sample, and then the soil colour can be sensed using an LDR colour sensor to predict the phosphorus (P), nitrogen (N), and potassium (K) values. These collected data together were stored in Firebase cloud storage media. Then, an Android application was developed to fetch and analyse the data from the Firebase cloud service from time to time by a farmer. In this study, a novel approach was identified via the hybridisation of algorithms. We have developed an algorithm using a multi-class support vector machine with a directed acyclic graph and optimised it using the fruit fly optimisation method (MSVM-DAG-FFO). The highest accuracy rate of this algorithm is 0.973, compared to 0.932 for SVM, 0.922 for SVM kernel, and 0.914 for decision tree. It has been observed that the overall performance of the proposed algorithm in terms of accuracy, recall, precision, and F-Score is high compared to other methods. The IoTSNA-CR device allows the farmer to maintain his field soil information easily in the cloud service using his own mobile with minimum knowledge. Additionally, it reduces the expenditure to balance the soil minerals and increases productivity.

Development of the control system for LEGO Mindstorms EV3 mobile robot based on MATLAB/Simulink elements

The Mindstorms EV3 robot, developed by LEGO, is one of the popular robots that has been widely used in various fields. Unlike previous versions of mobile LEGO robots, EV3 allows the development of real-time applications for teaching a variety of subjects, as well as for conducting research experiments. The object of research in this case is the Mindstorms EV3 robot connected to MATLAB/Simulink. The design consists of a controller, one color sensor, two servo motors and one support wheel. Each servo motor is built on a DC collector motor with a matching gearbox and has the ability to measure the number of revolutions corresponding. A digital sensor with a sampling frequency of 1 kHz is used as a color sensor, which can determine the color or brightness of light. Despite its popularity, the EV3 robot control system in interaction with the MATLAB/Simulink programming environment is a rather complex solution and therefore requires further research. The scientific part of the research focuses on discovering the regularities of the Mindstorms EV3 control system, developing a control system model, and exploring the potential of MATLAB/Simulink to expand the robot's capabilities. An analysis of the main elements of the control system, such as sensors and servos, was carried out. The graphs of the dependences of the characteristics of the servo drives were built and the efficiency of the robot movement was checked depending on the parameters set in the program. The result of the development of the mobile robot control system was the adjustment of the mobile robot movement regulators along a given trajectory in the form of a drawn line, which allowed estimating the maximum permissible speed of the robot movement. The presented research and development of a control system based on MATLAB/Simulink elements allows using the proposed method to control a mobile robot with high precision, analyze and verify the robot's electromechanical parameters in real time. This control system has a high potential and can practically be integrated into industrial objects of mobile robotics, provided types the sensors and executive mechanisms of the mobile robot match.

IoT-Based Banknotes Saving Automation System

Many problems occur with traditional savings, such as users not knowing the nominal amount that has been saved later, and the authenticity of the money saved. To overcome this problem, the "Savings Storage Automation System with IoT-Based Banknotes" tool was created, this tool aims to help users save money easily and safely. The system design is based on Arduino Mega. The inputs on the Arduino Mega consist of a GY-33 TCS34725 as a color sensor and a keypad to perform functions. The output produced is in the form of nominal data of banknotes that can be seen by the user through an LCD and sent to a database, checking the authenticity of the money is done manually by using ultraviolet. The method used to identify the nominal banknotes is based on the reading of each RGB value and color temperature on each nominal banknote. The average accuracy of reading currency values on real banknotes reaches 88%, while in testing counterfeit banknotes it is 14%. Testing execution time when entering money into savings is 3.57 seconds and data to the database is 6.57 seconds.

Statistical issues. Comment on “proximal sensor data fusion for tropical soil property prediction: Soil fertility properties”

dos Santos Teixeira et al. evaluated the effectiveness of three different sensors, portable X-ray fluorescence (pXRF) spectrometry, visible near-infrared (Vis-NIR), and NixProTM color sensor, in predicting six soil properties including sum of bases (SB), effective cation exchange capacity (eCEC), potential cation exchange capacity (CEC), base saturation (BS), soil organic matter (SOM) content, and soil pH, and compared their performance when used alone or in combination. Preprocessing methods and other variables were also analyzed. In this comment paper, some misuses of Kappa statistic in the original paper by dos Santos Teixeira et al. are discussed. The authors calculated Cohen's kappa coefficient to make categorical predictions for soil fertility classes. However, using Cohen's Kappa to estimate the intra-rater reliabilities (IRRs) is not appropriate on the condition of ordinal categorical variables in four or five categories in the original paper. We briefly introduce the Kappa statistic for evaluation of IRR between two raters and propose to use weighted Kappa to compute IRRs under such conditions. To conclude, we recognize that this does not significantly alter the conclusions of this paper, but it is necessary to ensure that the appropriate statistical tools are used.

Revised chlorophyll-a algorithms for satellite ocean color sensors in the East/Japan Sea

The East/Japan Sea (hereafter, East Sea (ES)) is a semi-enclosed marginal sea and considered as a miniature ocean with oceanic features (e.g., currents, eddies, fronts, upwelling) and dynamic marine environments. The ES is significantly influenced by the global ocean warming with fast increases in sea surface temperature during the last several decades. The climate-induced environmental changes can affect marine ecosystem and food webs in the ES. Therefore, more accurate estimates in phytoplankton biomass in the ES with high spatial and temporal resolutions are necessary to investigate phytoplankton production, fisheries, and carbon budget as well as biological responses to the environmental and climate changes. The NASA ocean chlorophyll-type (OCx) chlorophyll-a (Chl-a) algorithms based on blue to green band ratio for the satellite ocean color data in the global ocean have been evaluated using the in situ radiometric and Chl-a measurements in the ES. The model-derived Chl-a data using the OC Chl-a algorithms present systematic overestimation in the lower Chl-a concentrations between about 0.1 and 1.0 mg/m3 in the ES (with ∼40% uncertainties). New Chl-a algorithms for the ES has been derived from the 3rd polynomial regression fits of in situ Chl-a and maximum band ratios of remote sensing reflectance (Rrs). The revised Chl-a algorithms considerably improve the Chl-a data in the ES from the satellite ocean color data in the lower Chl-a concentrations. Thus, the new Chl-a algorithms can provide more accurate assessments in biological and biogeochemical processes such as primary productivity, phytoplankton phenology, phytoplankton dynamics, and carbon cycles.

Selection Of Tomato Fruits by Color and Size Based on Arduino

Human life relies heavily on agriculture. To fulfill the needs of human life every day, the process of planting, land, and harvest is fast. Short time and limited power are challenges that farmers should face. Similarly, the problem faced by tomato farmers is that they should be sorting tomatoes based on their level of maturity. The tomato farmers should be sorted by maturity level. The issue is a system of tomato partitioning based on maturity level. The purpose of this research is to design a tool to build the maturity of tomato fruit using Arduino. The tools used are color sensors TCS3200, HC-SR04 Ultrasonic, servo motors, and DC motors. The sorting system is designed using the principle of detecting red green blue (RGB) values of tomato fruit using color sensors. After that, the next system streams the tomatoes to the container according to the level of the decline by opening and closing the portal that is connected to the servo motor. The Portal leads to a ripe container if the fruit is ripe and vice versa if raw fruit leads to a raw container. Results obtained that the sorting tool is able to give a success rate of 90% in distinguishing ripe tomatoes ready to harvest (basil) from raw fruit (green).

Angle of polarization calibration for omnidirectional polarization cameras.

Polarization cameras quantify one of the fundamental properties of light and capture intrinsic properties of the imaged environment that are otherwise omitted by color sensors. Many polarization applications, such as underwater geolocalization and sky-based polarization compass, require simultaneous imaging of the entire radial optical field with omnidirectional lenses. However, the reconstructed angle of polarization captured with omnidirectional lenses has a radial offset due to redirection of the light rays within these lenses. In this paper, we describe a calibration method for correcting angle of polarization images captured with omnidirectional lenses. Our calibration method reduces the variance of reconstructed angle of polarization from 76.2 ∘ to 4.1 ∘. Example images collected both on an optical bench and in nature, demonstrate the improved accuracy of the reconstructed angle of polarization with our calibration method. The improved accuracy in the angle of polarization images will aid the development of polarization-based applications with omnidirectional lenses.

The color of aerosol particles

Digital in-line holography (DIH) is an established method to image small particles in a manner where image reconstruction is performed computationally post-measurement. This ability renders it ideal for aerosol characterization, where particle collection or confinement is often difficult, if not impossible. Conventional DIH provides a gray-scale image akin to a particle’s silhouette, and while it gives the particle size and shape, there is little information about the particle material. Based on the recognition that the spectral reflectance of a surface is partly determined by the material, we demonstrate a method to image free-flowing particles with DIH in color with the eventual aim to differentiate materials based on the observed color. Holograms formed by the weak backscattered light from individual particles illuminated by red, green, and blue lasers are recorded by a color sensor. Images are reconstructed from the holograms and then layered to form a color image, the color content of which is quantified by chromaticity analysis to establish a representative signature. A variety of mineral dust aerosols are studied where the different signatures suggest the possibility to differentiate particle material. The ability of the method to resolve the inhomogeneous composition within a single particle in some cases is shown as well.

Colorimetric Gas Detection Using Molecular Devices and an RGB Sensor

Spectrophotometry and colorimetry are among the most-used techniques for chemical and biological analyses, but the required equipment is often expensive and restricted to laboratory use. We present here a low-cost and portable color measuring device that can provide similar results to laboratory spectrophotometers in color measuring applications. Our prototype was based on an RGB color sensor interfaced to a Raspberry Pi and mounted on custom sample holders with a dual illumination source for reflectance or transmittance measurements. To evaluate its capabilities for the detection of gases, we used two already-tested colorimetric molecular devices: Harrison’s reagent supported on porous TiO2 films for the detection of phosgene, and mixed films of a porphyrinic metal–organic frameworks and polydimethylsiloxane for the detection of biogenic amines. The results showed that the prototype could accurately monitor the color change of the sensing devices when exposed to the analytes and that its versatility allowed for the measurement of samples with different characteristics. This inexpensive and portable prototype, able to run on a 5 V battery and work wirelessly, proved to be a valid alternative for color measuring when expensive spectrophotometers are not available, mobility is needed, or a full-spectral characterization is not necessary.

Performance-enhanced regenerated cellulose film by adding grape seed extract

Eco-friendly packaging material with intelligent colorimetric performance has been a requirement for food safety and quality. This work focused on a food packaging material from regenerated cellulose films that added the grape seed extract (GSE) and polyethylene glycol 200 (PEG). FTIR and SEM techniques were employed to prove the compatibility of GSE with cellulose matrix. The composite film showed an enhanced elongation at break (16.61 %) and tensile strength (33.09 MPa). The addition of PEG and GSE also improved the water contact angle of regenerated-cellulose film from 53.8° to 83.8°. Moreover, the composite films exhibited UV-blocking properties while maintaining adequate transparency. The GSE induced the regenerated films with a macroscopic change in color under different pH conditions. Furthermore, the loading of GSE slowed down the decomposition of strawberries and delayed the self-biodegradation compared with the control for more than 3 days and 18 days. The present study showed a regenerated cellulose film with acceptable mechanical and hydrophilia properties, pH-responsiveness, anti-decomposition, and delayed biodegradation performances, indicating a potential color sensor in food packaging.

A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes

The land-affected signal in remotely sensed radiance from nearshore waters is a common problem for remote sensing, introducing uncertainty in atmospheric correction and subsequent water quality constituent concentration estimates. This study proposes a new method for identifying effects of land on satellite remote sensing of water quality. The new optical water types (OWT) containing the land-affected signal were derived from POLYMER-corrected imagery of the Medium Resolution Imaging Spectrometer in reduced resolution (MERIS RR) and Sentinel-3 Ocean and Land Colour Instrument (OLCI). These were then applied, as part of a larger set of existing OWTs corresponding to the variability observed in natural waters, to satellite images. The ability to identify pixels containing both water and land, and those contaminated with radiance from adjacent land, was evaluated. Our test sites include dark lakes of varying size in Sweden (Lakes Rusken, Bolmen, Ringsjön, and Ivösjön) where the classification showed high sensitivity to land near the lake shore. The land-affected signal is shown to lead to underestimations of chlorophyll-a concentration and Forel-Ule colour indices, and overestimations of turbidity in these lakes, which can be corrected after masking out the land-affected pixels. The land-affected signal is strongest in summer, both NDVI and sun zenith angle covaried with the seasonal variation of land-affected signal. Further, the results confirmed that satellite images with coarser spatial resolution are more prone to land-affected signal compared to images with finer spatial resolution, for small inland water bodies. We propose a data-driven approach for water quality processing with ‘land-affected water types’ as an effective way to improve the lake optical water quality monitoring from water colour sensors.

FUZZY SYSTEM AS FOUR-JOINT ROBOT MOVEMENT CONTROL FOR MOVING GOODS BASED ON TIME AND OBJECT COLOR

Technology Disruption has driven the acceleration of the industrial era 4.0 which is based on thespeed of data-based systems and artificial intelligence. Another impact is intelligent robots as asubstitute for human performance, because humans experience fatigue and decrease accuracywith increasing working time. Robots are an alternative whose presence cannot be avoided,therefore the robot's work system must be improved to optimize its speed and accuracy. One ofthe implementations of the robot is as a product moving machine from the initial coordinates (Pick)to the destination coordinates (Place). This study uses a four-joint robot (4 DOF) with a producttransfer mechanism that can be adjusted based on the input priority that has been set using theFuzzy Logic Control (FLC) method which is implemented using an Arduino Mega 2560microcontroller, and the robot used is Dobot Magician. There are 2 input variables used, namelythe mass of the object detected by the Weight sensor Load Cell + HX711 module and the colorof the object detected using the TCS3200 color sensor. The output variable is the movement ofthe robot to the Place which can be analyzed by calculating the position matrix using the Matlabprogram.

A photodynamic color sensor using diacetylene vesicles for the rapid visualization of singlet oxygen

Despite therapeutic benefits of singlet oxygen (1O2), the development of reliable 1O2 detection methods is challenging due to its short lifespan and fast reactivity. Herein, we report a photodynamic color sensor for visualizing photoinduced 1O2 using a 10,12-pentacosadiynoic acid (PCDA) vesicle and a mini singlet oxygen generator (miniSOG). The miniSOG produced 1O2 by photoactivation, which subsequently reacted with the PCDA vesicle, resulting in a rapid color change from colorless monomeric PCDA to blue-colored polydiacetylene (PDA) vesicles (from yellow to green in the miniSOG-mixed solutions) through electron delocalization-mediated polymerization. Therefore, the color change from PCDA to PDA enabled the rapid and simple detection of photoinduced 1O2 and its scavengers on a microplate. Notably, the PCDA vesicles exhibited better sensitivity and stability for photoinduced 1O2 than classical chromogenic reagents. Along with the unique chromatic property and structural versatility of PCDA, we suggest that our approach is highly suitable for visualizing 1O2 in a rapid and simple way.