Blue Color Model Research Articles

Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

Abstract Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.

Effect of Membrane Removal on the Production of Calcium Oxide from Eggshells via Calcination

In the pursuit of a circular economy and against a background of viable alternative uses of avian eggshells and eggshell membranes, this research investigated the effect of domesticated chicken eggshell membrane separation on the calcination process and quality of Calcium Oxide (CaO) produced. Acetic acid was used to leach the eggshells for 60 min to aid separation of the shell membrane. X-ray diffraction, red green blue colour model, and thermogravimetric analyses were used to characterize the calcined shells. Leached and separated shells produced CaO of comparable quality and crystallinity to unseparated shells after calcination. Results showed that while leaching and membrane separation did not affect calcination temperature, residual ash from the membrane left impurities in the unseparated shells after calcination. It was concluded that leaching and separating the membranes from eggshells prior to calcination serves to upcycle eggshells by facilitating improved CaO production and membrane value extraction.

Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study

Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering 7 × 7 window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre- and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.

New application of old methods: Development of colorimetric sensor array based on Tollen’s reagent for the discrimination of aldehydes based on Tollen’s reagent

Qualitative and quantitative testing of aldehydes is meaningful for chemical toxin detection, food inspection, and disease monitoring. Herein, we reported a simple, accurate, and selective Tollen’s reagent-based colorimetric sensor array for determination and detection of aldehydes. Three kinds of negatively charged gold nanoparticles (Au NPs) with different sizes (13, 22, and 40 nm) were synthesized and characterized by transmission electron microscopy and zeta potential measurement. In the presence of aldehydes, Ag+ from Tollen’s reagent was attracted by the negative charge on the surface of Au NPs. Ag+ was reduced into Ag0 in situ, forming Au@Ag core–shell nanostructure and resulting in a significant color change. Detailed morphological and dimensional changes were observed by transmission electron microscopy. ΔRGB values (the value changes in the red, green, and blue color model) of Au NPs were captured as the optical signal for further data processing. Results of pattern recognition indicated the outstanding discrimination performance of the system for identification of aldehydes. Moreover, the array possessed quantitative detection capability for formaldehyde, selectivity, and reproducibility and thus has great potential in practical detection.

Metal ions regulated Ag NPRs etching colorimetric sensor array for discrimination of Chinese Baijiu

One quick and exact Baijiu identification is meaningful in production and market management. Herein, we report a metal ions regulated Baijiu colorimetric sensor array. This is the first time metal ions used to regulate etching microenvironment of Ag nanoprism (Ag NPRs) in Baijiu discrimination sensor array. Edges of Ag NPRs are easier to be etched than other regions, in which morphology transition from nanoprism to nanodisk. The addition of different metal ions lead to the change of the Ag NPRs etching microenvironment in Baijiu forming different etching morphologies with dazzling colors. Transmission electron microscope with energy spectrum analysis (TEM-ESA) were employed to exhibits general process. RGB (the red, green and blue color model) values of final Ag NPRs were captured as the optical single for the further data processes. Pattern recognition and neural network simulation shows the outstanding discrimination ability of provided array for 16 Baijiu samples. Furthermore, selectivity experiments, which are not available in other paper, shows that our arrays have considerable selectivity and have great potential in practical detection.

Lights, Camera, Spectroscope! The Basics of Spectroscopy Disclosed Using a Computer Screen

The generation of secondary colors in digital devices by means of the additive red, green, and blue color model (RGB) can be a valuable way to introduce students to the basics of spectroscopy. This work has been focused on the spectral separation of secondary colors of light emitted by a computer screen into red, green, and blue bands, and how the intensity of these bands can be modulated if the portions of each primary color are modified in the RGB coordinates. The option found in the PowerPoint program for defining RGB values in the background of slides has been used in order to tune the color of the analyzed light. On the other hand, a CD-ROM based spectroscope has been found to provide enough resolution for this kind of analysis and an accessible way to perform it. These studies can be carried out qualitatively, comparing the different spectra observed through the spectroscope, as well as quantitatively, if these spectra are photographed using a digital camera and they are plotted after the analysis o...

FEATURE EXTRACTION OF COLON AND RECTUM CANCER FROM MRI IMAGES

Feature extraction, the third major component plays a vital role on the final expected outcome of the application using digital image processing. Feature plays a very significant role in the area of digital image processing. The image feature detection and extraction are based on the colour, shape and texture feature categories. Colour feature refers to the presence of one of the colours either in RGB space (Red, Green, Blue colour model) or in HSxspace (Hue, Saturation, Value/Brightness model) in the particular area of interest. Especially in medical imaging, the internal parts will have a specific colour slightly varying in hue. When the scan modality provides a colour image, colour feature can be used for processing. The structure of the organs and its parts if visible clearly, then the shape parameter can be used for classification of images. The knowledge of the domain experts will enable to find the structure as what and where it is located. Similarity in shape will help in identifying the objects. Irrespective of colour and shape, texture parameter plays an important role in classification problems. In the absence of colour and shape, the only available feature is the textural features of the object. It looks into the properties related with intensity values of each pixel. This paper proposes the textural feature extraction techniques that best suits for retrieving the properties of colon and rectum cancer MRI images which can be further processed for classification.

UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands.

The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as examples. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 samples were extracted from the obtained data set and labelled into six classes. Segmentation results provided an individual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation.

Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning

Human activity recognition requires both visual and temporal cues, making it challenging to integrate these important modalities. The usual schemes for integration are averaging and fixing the weights of both features for all samples. However, how much weight is needed for each sample and modality, is still an open question. A mixture of experts via a gating Convolutional Neural Network (CNN) is one promising architecture for adaptively weighting every sample within a dataset. In this paper, rather than just averaging or using fixed weights, we investigate how a natural associative cortex such as a network integrates expert networks to form a gating CNN scheme. Starting from Red Green Blue color model (RGB) values and optical flows, we show that with proper treatment, the gating CNN scheme works well, indicating future approaches to information integration in future activity recognition.

Algorithmic clustering of LiDAR point cloud data for textural damage identifications of structural elements

This study explored the potential of combining point cloud data (PCD) and data clustering algorithms for textural damage detection of commonly seen structural elements in Taiwan. Intensity and RGB (red, green, and blue color model) information acquired by ground LiDAR (light detection and ranging) were used for clustering analysis. Four data clustering algorithms, k-means (KM), fuzzy c-means (FCM), subtractive clustering (SC), and density-based spatial clustering of applications with noise (DBSCAN) were employed to detect the textural damages and to compare the corresponding efficiency and accuracy. The structural elements being studied were rusted rolling doors representing general metal materials with corrosion, walls with tile spall off representing structural elements with erosions and physical damages, and washing finish walls with water staining representing the aging and lichen covering of structural elements. Our study results suggested that both KM and FCM gave preferable clustering performance than SC and DBSCAN. They exhibited desired accuracy for the damage/anomaly identification as well as computational efficiency, suggesting that KM and FCM were more appropriate for these types of application when PCD was used. It was also concluded that intensity rather than RGB data was more appropriate for reflecting the damaged areas. Intensity data was less interfered by environmental effects such as sunlight and rainwater when used to detect the textural changes. The clustering results were also shown to be associated with the clustering number and with the nature of the textural damage type.

Automatic CME front edge detection from STEREO white‐light coronagraph images

AbstractThe coronagraph images captured by a Solar Terrestrial Relations Observatory (STEREO) Ahead/Behind (A/B) spacecraft allow tracking of a coronal mass ejection (CME) from two different viewpoints and reconstructing its propagation in three‐dimensional space. The reconstruction can be done using a triangulation technique that requires a CME front edge location. There are currently no robust automatic CME front edge detection methods that can be integrated with the triangulation technique. In this paper, we propose a novel automatic method to detect the front edge of the CME using STEREO coronagraph 2 red‐colored Red, Green, Blue color model images. Our method consists of two modules: preprocessing and classification. The preprocessing module decomposes each coronagraph image into its three channels and uses only the red channel image for CME segmentation. The output of the preprocessing module is a set of segmented running‐difference binary images which is fed into the classification module. These images are then transformed into polar coordinates followed by CME front edge detection based on the distance that CME travels in the field of view. The proposed method was validated against a manual method using total 56 CME events, 28 from STEREO A and 28 from STEREO B, captured in the period from 1 January 2008 to 16 August 2009. The results show that the proposed method is effective for CME front edge detection. The proposed method is useful in quantitative CME processing and analysis and will be immediately applicable to assist automatic triangulation method for real‐time space weather forecasting.