Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

MoFlus: An Open-Source Android Software for Fluorescence-Based Point of Care

High-sensitivity fluorescence-based tests are utilized to monitor various activities in life science research. These tests are specifically used as health monitoring tools to detect diseases. Fluorescence-based test facilities in rural areas and developing countries, however, remain limited. Point-of-care (POC) tests based on fluorescence detection have become a solution to the limitations of fluorescence-based tools in developing countries. POC software for smartphone cameras was generally developed for specific devices and tools, and it ability to select the desired region of interest (ROI) is limited. In this work, we developed Mobile Fluorescence Spectroscopy (MoFlus), an open-source Android software for camera-based POC. We mainly aimed to develop camera-based POC software that can be used for the dynamic selection of ROI; the number of samples; and the types of detection, color, data, and for communication with servers. MoFlus facilitated the use of touch screens and data given that it was developed on the basis of the SurfaceView library in Android and Javascript object notation applications. Moreover, the function and endurance of the app when used multiple times and with different numbers of images were tested.

Effect of Data Augmentation in the Classification and Validation of Tomato Plant Disease with Deep Learning Methods

The paper discusses disease identification and classification in tomato plants, as well as the effect of data augmentation in deep learning models. The database used here is Tomato plant leaves (TPL) images from the PlantVillage Database in the healthy and disease classes. The disease categories have been chosen depending on their occurrence in the Indian States. The proposed ResNet50, ResNet18, and ResNet101 deep-learning model with transfer learning combined with the softmax classification are used to identify and categorize the tomato leaf images into the healthy or diseases classes in the dataset. The unique combination of including the noise and blur in the images and position and color data augmentation makes the dataset robust. Two different data augmentation methods are used for the classification problem, and significant improvement is seen in the classification accuracy with the proposed augmented dataset. The model’s success rate makes the model helpful in extending support in validating a model for identifying plant disease. The validation of models is done on PlantVillage and images taken at Krishi Vigyan Kendra Narayangaon, Pune, India. ResNet101 model trained with augmented dataset outperforms the testing accuracy of 99.99% and validation accuracy of 95.83%.

The Development of Digital Image Colorimetric Quantitative Analysis of Multi-Explosives Using Polymer Gel Sensors

Polymer gel sensors on 96-well plates were successfully used to detect four different multi-explosives, including 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), nitrite, and perchlorate. The products of reactions between the explosives and the polymer gel sensors were digitally captured, and the images were analyzed by a developed Red–Green–Blue (RGB) analyzer program on a notebook computer. RGB color analysis provided the basic color data of the reaction products for the quantification of the explosives. The results provided good linear range, sensitivity, limit of detection, limit of quantitation, specificity, interference tolerance, and recovery. The method demonstrated great potential to detect explosives by colorimetric analysis of digital images of samples on 96-well plates. It is possible to apply the proposed method for quantitative on-site field screening of multi-explosives.

Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods

In the directed energy deposition (DED) process, significant empirical testing is required to select the optimal process parameters. In this study, single-track experiments were conducted using laser power and scan speed as parameters in the DED process for titanium alloys. The results of the experiment confirmed that the deposited surface color appeared differently depending on the process parameters. Cross-sectional view, hardness, microstructure, and component analyses were performed according to the color data, and a color suitable for additive manufacturing was selected. Random forest (RF) and support vector machine multi-classification models were constructed by collecting surface color data from a titanium alloy deposited on a single track; the accuracies of the multi-classification models were compared. Validation experiments were performed under conditions that each model predicted differently. According to the results of the validation experiments, the RF multi-classification model was the most accurate.

Analysis of cassava chip image characterization during drying process

Monitoring method during food processing is an indispensable activity in the industry of food processing. A digital image processing technique is one of the methods to process images into information in the form of product physical condition. This study aimed to monitor the changes in cassava chips image characteristics through the images along the drying process. The image characteristic i.e covered color, texture, and area. The images were captured by using Webcam type Logitech C525 8.0 megapixel autofocus per minute. Then, the result of these images was processed to get color data of R, G, B, H, S, I, L, a*, b* and the texture i.e. energy, homogeneity, contrast, entropy, and to identify chips size was processed by the number of pixels of the image. While the data about the mass changes along the drying process were taken per minute from a digital scale. The results of this study showed that the length of drying made the value of R, G, B, H and I decreased, but the value of S contrastively increased. The area or the number of image pixels declined dramatically in 1 hour of drying, later (after one hour of drying) the decline was almost zero.

A study on physical characteristics of dried bio-slurry produced in tropical condition through treatment combination of drying and turning period

The research aims to evaluate the physical characteristics of dried bio-slurry produced by treatment combination of drying and turning period in tropical conditions. Research material used fixed-dome digester model with a capacity of 12 m3 and cattle dung from Simmental crossbreed. Physical characteristics of bio-slurry were obtained by combining treatments between drying period (15 and 30 days) and turning period (each turning process in 7 and 10th day) as follows: T1 (15 days and 7th day), T2 (15 days and 10th day), T3 (30 days and 7th day), T4 (30 days and 10th day). The chemical compound of fresh bio-slurry was analyzed as initial information. The humidity, temperature, and color data were analyzed using analysis of variance and further analyzed using Tukey’s test. Also, the chemical compound and pH used descriptive analysis. The study obtained that the chemical composition of fresh bio-slurry was moisture content (89.53%), C-organic (37.27%), nitrogen (48.92ppm), phosphor (1.71%), potassium (3.89%), and C/N ratio (7.454). Besides, the treatment showed a significant difference (P<0.01) in humidity and color. Temperature dan pH of dried bio-slurry remained constant at 29.10-29.270C and 7 respectively. Therefore, treatment combinations can be applied to make dried bio-slurry as fertilizer in tropical conditions.

Study of the Application of Terrestrial Laser Scanning for Identification of Pathologies in Concrete Structures

Laser scanning enhances classic field surveys. The terrestrial laser scanner is a versatile device with applications in various areas of knowledge, which uses remote sensing fundamentals to determine point coordinates. It is a remote, active, noninvasive, nondestructive and high-precision technique to capture reality that records from thousands to millions of points per second in a detailed representation of the situation called a point cloud. The surveys are performed along the object of interest in a process called scanning, which has as its gross product a dense cloud of three-dimensional points of the scanned object. This point cloud stores information about the object’s geometry, return pulse intensity, and point color data. As a way of extending the uses of terrestrial laser scanning, this work studies the application of this method in civil engineering, through the identification of pathologies in reinforced concrete structures, aiming to show how geoinformation can be employed in this area. To this end, a case study of the São Cristóvão Viaduct was conducted in the city of Rio de Janeiro. This study included definition of the site of analysis; planning and execution of the field survey to collect raw data; processing of the point cloud; and generation of a three-dimensional surface for global visualization of the structure and identification of pathological manifestations and the regions where they were observed. Concrete structures in general are affected by various external factors, such as weather and anthropogenic actions, which contribute to their wear.

Forensic DNA Phenotyping: starting point to prediction model in Pernambuco population, Brazil

The study of Externally Visible Characteristics (EVC) of pigmentation associated with SNPs (Single Nucleotide Polymorphisms) has become a target in the forensic field due to the possibility of phenotypically characterizing an individual. In Brazil, there are few data that shows the evaluation of some these markers, so further studies are necessary to understand better the pigmentation process related to genetic markers. The aim of this study was to test the association between 8 SNPs present in HIrisplex tool and EVC to provide a starting point for the development of prediction models for heterogeneous populations like the one in Pernambuco. Were evaluated 176 individuals by associations between self-reported eye, hair and skin color data and polymorphisms. Artificial intelligence tools were used for the prediction models. Significant associations were found between rs1800404 (OCA2), rs6058017 (ASIP), rs16891982 (SLC45A2) and rs1426654 (SLC24A5) with (EVC). The prediction models evaluated showed satisfactory prediction rates, rates above 60% for skin color and above 70% for eyes and hair. The associations found in our data show the importance of SNPs evaluation used in DNA Phenotyping, because of its ability to provide new information in the context of criminal investigations. Our data indicate that is possible to use molecular information to predict phenotypes in miscigenated populations, like the Brazilian population. These polymorphisms could be possible phenotypic predictors for the Pernambuco population.