RGB Image Analysis Research Articles

Assessment of Drought Response in Kimchi Cabbage, Radish, and Lettuce Seedlings Using RGB Image Analysis

Assessment of Drought Response in Kimchi Cabbage, Radish, and Lettuce Seedlings Using RGB Image Analysis

Development of ExG, ExR, ExGR, HSV, CIELAB Images from RGB Images Using Image Segmentation Algorithm in Computer Vision Based Herbicide Spraying Applications

Weed management in agriculture is critical for preventing crop yield losses, with traditional methods often leading to environmental harm and increased production costs. This study explores the development of color indices and models for weed detection in computer vision-based herbicide spraying applications. Among the all sensors, RGB colour cameras offer several advantages, including low cost and wide availability of image processing libraries tailored for RGB image analysis. In present study a Logitech C270 webcam was used for acquiring the RGB images and a specially python algorithm was developed for image segmentation based on color. Excess Green (ExG), Excess Red (ExR), Excess Green minus Red (ExGR), HSV, and CIELAB color model images are developed by using algorithm. The research demonstrates that while ExG and ExGR indices are effective under specific conditions, the CIELAB model offers superior segmentation results across varying lighting environments. Among all these color indices and models, particularly CIELAB, can enhance the precision of weed detection in automated herbicide applications, thereby reducing environmental impact and improving agricultural efficiency.

Innovative PNA-LB mediated allele-specific LAMP for KRAS mutation profiling on a compact lab-on-a-disc device

Tailored healthcare, an approach focused on individual patients, requires integrating emerging interdisciplinary technologies to develop accurate and user-friendly diagnostic tools. KRAS mutations, prevalent in various common cancers, are crucial determinants in selecting patients for novel KRAS inhibitor therapies. This study presents a novel state-of-the-art Lab-on-a-Disc system utilizing peptide nucleic acids-loop backward (PNA-LB) mediated allele-specific loop-mediated isothermal amplification (LAMP) for detecting the frequent G12D KRAS mutation, signifying its superiority over alternative mutation detection approaches. The designed Lab-on-a-Disc system demonstrated exceptional preclinical and technical precision, accuracy, and versatility. By applying varying cutoff values to PNA- LB LAMP reactions, the assay's sensitivity and specificity were increased by 80 % and 90 %, respectively. The device's key advantages include a robust microfluidic Lab-on-a-Disc design, precise rotary control, and a cutting-edge induction heating module. These features enable multiplexing of LAMP reactions with high reproducibility and repeatability, with CV% values less than 3.5 % and 5.5 %, respectively. The device offers several methods for accurate endpoint result detection, including naked-eye observation, RGB image analysis using Python code, and time of fluorescence (Tf) values. Preclinical specificity and sensitivity, assessed using different cutoffs for Eva-Green fluorescence Tf values and pH-sensitive dyes, demonstrated comparable performance to the best standard methods. Overall, this study represents a significant step towards tailoring treatment strategies for cancer patients through precise and efficient mutation detection technologies.

Vegetation Land Segmentation with Multi-Modal and Multi-Temporal Remote Sensing Images: A Temporal Learning Approach and a New Dataset

In recent years, remote sensing analysis has gained significant attention in visual analysis applications, particularly in segmenting and recognizing remote sensing images. However, the existing research has predominantly focused on single-period RGB image analysis, thus overlooking the complexities of remote sensing image capture, especially in highly vegetated land parcels. In this paper, we provide a large-scale vegetation remote sensing (VRS) dataset and introduce the VRS-Seg task for multi-modal and multi-temporal vegetation segmentation. The VRS dataset incorporates diverse modalities and temporal variations, and its annotations are organized using the Vegetation Knowledge Graph (VKG), thereby providing detailed object attribute information. To address the VRS-Seg task, we introduce VRSFormer, a critical pipeline that integrates multi-temporal and multi-modal data fusion, geometric contour refinement, and category-level classification inference. The experimental results demonstrate the effectiveness and generalization capability of our approach. The availability of VRS and the VRS-Seg task paves the way for further research in multi-modal and multi-temporal vegetation segmentation in remote sensing imagery.

A Low-Cost Microfluidic-Based Detection Device for Rapid Identification and Quantification of Biomarkers-Based on a Smartphone.

The sensitive and rapid detection of microsamples is crucial for early diagnosis of diseases. The short response times and low sample volume requirements of microfluidic chips have shown great potential in early diagnosis, but there are still shortcomings such as complex preparation processes and high costs. We developed a low-cost smartphone-based fluorescence detection device (Smartphone-BFDD) without precision equipment for rapid identification and quantification of biomarkers on glass capillary. The device combines microfluidic technology with RGB image analysis, effectively reducing the sample volume to 20 μL and detection time to only 30 min. For the sensitivity of the device, we constructed a standard sandwich immunoassay (antibody-antigen-antibody) in a glass capillary using the N-protein of SARS-CoV-2 as a biological model, realizing a low limit of detection (LOD, 40 ng mL-1). This device provides potential applications for different biomarkers and offers wide use for rapid biochemical analysis in biomedical research.

Tailoring Acid Free-Paper based Analytical Devices (Af-PADs) via radiation assisted modification of cellulose paper

A novel green fabrication approach is being proposed based on radiation assisted modification of Whatman filter paper 1 (WFP) for development of Acid Free-Paper based Analytical Devices (Af-PADs). Af-PADs exude immense potential as handy tools for on-site detection of toxic pollutants such as, Cr(VI), boron, etc., which have established detection protocols involving acid mediated colorimetric reactions that necessitate external acid addition. The proposed Af-PAD fabrication protocol asserts its novelty through elimination of external acid addition step, making the detection process safer and simpler. To achieve this, poly(acrylic acid) (PAA) was grafted onto WFP via a single step, room temperature process of gamma radiation induced simultaneous irradiation grafting, introducing acidic -COOH groups in the paper thereon. Grafting parameters namely, absorbed dose and concentrations of monomer, homopolymer inhibitor and acid were optimized. The -COOH groups incorporated in PAA-grafted-WFP (PAA-g-WFP) provide localized acidic conditions for colorimetric reactions between pollutants and their sensing agents, anchored on the PAA-g-WFP. Af-PADs loaded with 1,5-diphenylcarbazide (DPC) have been ably demonstrated for visual detection and quantitative estimation of Cr(VI) in water samples using RGB image analysis, with LOD value of 1.2 mg.L−1 and a measurement range comparable to that of commercially available PADs based Cr(VI) visual detection kits.

Pig Weight Estimation According to RGB Image Analysis

In pig farming, knowing the exact weight of each animal is critical for the owner. Such information can help determine the amount and type of feed that needs to be fed to a specific fattening pig. Weighing pigs has always been problematic, because it is highly time consuming, and herding the pigs on the scale is extremely cumbersome. Moreover, it causes stress to the animals. The aim of our study was to build an RGB-based system that could estimate the daily weight of pigs and individual animal weight. The study was set up in a 100-day rotation in a commercial pig farm where we monitored 32 pigs. We developed a system to identify the features of the pigs, more particularly the head, shoulder, belly, and rump part. Three different modelswere tested, and their main differences were linked to image processing and training data. Using these models, we received higher than 97% accuracy between the predicted and the manually recorded weight of the animals. This system allows owners to manage and monitor their pigs using our web interface, allowing them to make crucial decisions during the farming process.

Novel apparatus for monitoring simultaneously color and °Brix of selected fruit juices through RGB image analysis

AbstractThis work introduces a novel apparatus for monitoring both color and °Brix of selected fruit juices. These parameters are essential for quality and processing control at concentration and dilution operations. The proposed apparatus performs the red, green, and blue (RGB) image analysis of the fruit juice as it flows through a transparent cell equipped with a webcam and a light source. The viewing cell is inside a box for controlling temperature and for ensuring homogeneous lighting. This apparatus not only measures the RGB color space of the fruit juice online but also allows relating it to the °Brix. The relationship between RGB and °Brix was obtained by measuring the RGB color space of various fruit juice samples, of which the °Brix were previously determined using a digital refractometer. RGB image analysis showed an excellent relationship with °Brix (R2 > 0.99, using an exponential fit), allowing for monitoring reliably this parameter during continuous fruit juice concentration and dilution experiments. These results show the potential of this novel apparatus to substitute conventional instruments such as colorimeters and refractometers for online process monitoring. Furthermore, it has the advantage of not requiring sampling, which reduces measurement errors and saves time.Practical applicationsThis work introduces a novel apparatus based on RGB image analysis for monitoring simultaneously the color and the °Brix of fruit juices. These parameters are important to control the production and quality of fruit juices, especially at concentration and dilution operations. The apparatus proposed here is a viewing cell where the RGB color space is quantified using the software RGBview® as the fruit juice flows through it. The °Brix are determined from the RGB color space using a suitable fit obtained from a previous calibration with juice samples with known °Brix. This is an alternative to conventional analytical instruments such as colorimeters and refractometers but with the advantage of performing an online measurement. This configuration allows for the reduction of analytical errors associated with sampling and saves time. Moreover, this apparatus has the potential to monitor other physicochemical properties related to the color of fruit juices.

Low-cost, portable, on-site fluorescent detection of As(III) by a paper-based microfluidic device based on aptamer and smartphone imaging.

A turn-on fluorescent aptasensor based on a paper-based microfluidic chip was developed to detect arsenite via aptamer competition strategy and smartphone imaging. The chip was prepared by wax-printing hydrophilic channels on filter paper. It is portable, low-cost, and environmentally friendly. Double-stranded DNA consisting of aptamer and fluorescence-labeled complementary strands was immobilized on the reaction zone of the paper chip. Due to the specific strong binding between aptamer and arsenite, the fluorescent complementary strand was squeezed out and driven by capillary force to the detection area of the paper chip, so that the fluorescent signal arose in the detection area under the excitation wavelength of 488nm. Arsenite can be quantified by using smartphone imaging and RGB image analysis. Under the optimal conditions, the paper-based microfluidic aptasensor exhibited excellent linear response over a wide range of 1 to 1000nM, with a detection limit as low as 0.96nM (3σ).

Investigation of the applicability of RGB image analysis for the inspection of slope revegetation with herbaceous plants by seeding works

草本植生を用いた播種工の管理において定量的な指標となる植被率は，目視により計測されることが一般的であるため，主観的で曖昧な評価にとどまっている。そこで筆者らは，可視光画像解析技術を援用した植被率の計測手法に着目した。本研究では，植被率の計測結果のばらつきを抑えるための機器設定と，自然光源の変化が計測結果に与える影響について検討した。プログラムAEが採用されている機器の場合，ホワイトバランスをプリセットモードに固定し，ISO感度を最小にする設定が最適であるという結果が得られた。また，本研究における撮影条件下では，自然光源の変化によって植被率の計測結果は，撮影時刻や天気の違いにより±2.5～7.3%程度（95%予測区間）の影響を受けること，天気が変わらない同日中の撮影では標準偏差±1.8%程度で植被率の計測が可能であることを明らかにした。

An innovative technique for faecal score classification based on RGB images and artificial intelligence algorithms

AbstractThe milk production is strongly influenced by the dairy cow welfare related to a good nutrition and the analysis of the digestibility of feeds allows us to evaluate the health status of the animals. Through faeces’ visual examination it is possible to estimate the quality of diet fed in terms of lacking in fibre or too high in non-structural carbohydrates. The study was carried out in 2021, on four dairy farms in central Italy. The purpose of this work is the classification and evaluation of dairy cow faeces using RGB image analysis through an artificial intelligence (AI) (convolutional neural network (CNN)) algorithm. The main features to analyse are pH, colour and consistency. For the latter two RGB imaging was combined with deep learning and AI to reach objectivity in samples’ evaluation. The images have been captured with several smartphones and cameras, under various light conditions, collecting a data set of 441 images. Images acquired by RGB cameras are then analysed through CNN technology that extracts features and data previously standardized by a faecal score index assigned after a visual analysis and based on five classes. The results achieved with different training strategies show a training accuracy of 90% and a validation accuracy of 78% of the model which allow us to identify problems in bovine digestion and to intervene promptly in feed variation. The method used in this study eliminates subjectivity in field analysis and allows future improvement of increasing the data set to strengthen the model.

Fractal algorithms and RGB image processing in scribal and ink identification on an 1819 secret initiation manuscript to the “Philike Hetaereia”

Historical texts incorporate important characteristics that need to be assessed including genre, text structure and content. Often overlooked are characteristics of handwritten manuscripts commonly divided into legibility, readability and aesthetics. To determine the scientific feasibility of classification of handwritten texts an objective approach is developed to describe twenty handwritten pages of an 1819 Greek manuscript, that refers to the initiation to the Greek secret “friendly society” (Philike Hetaereia) organization, established as part of the Greek independence against the Ottoman Turks. It is investigated through a fractal and RGB image analysis approach. Fractal Minkowski Dimension was applied on the handwritten text and the RGB color analysis on the ink and paper and both were used as a non-invasive manner and revealed interesting results. The novel RGB image analysis and the fractal analysis of the manuscript identified respectively, five iron gall inks and four scribes from the ink content and handwritten styles, of the compact five lines text and whole text pages. The novel approach was verified with another old manuscript of known ink pigments, as well as with thirteen known handwritten texts of that period and four prints representing modern and similar period texts substantiating the findings of the novel methods.

Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media.

Otitis media (OM) is one of the most common ear diseases in children and a common reason for outpatient visits to medical doctors in primary care practices. Adhesive OM (AdOM) is recognized as a sequela of OM with effusion (OME) and often requires surgical intervention. OME and AdOM exhibit similar symptoms, and it is difficult to distinguish between them using a conventional otoscope in a primary care unit. The accuracy of the diagnosis is highly dependent on the experience of the examiner. The development of an advanced otoscope with less variation in diagnostic accuracy by the examiner is crucial for a more accurate diagnosis. Thus, we developed an intelligent smartphone-based multimode imaging otoscope for better diagnosis of OM, even in mobile environments. The system offers spectral and autofluorescence imaging of the tympanic membrane using a smartphone attached to the developed multimode imaging module. Moreover, it is capable of intelligent analysis for distinguishing between normal, OME, and AdOM ears using a machine learning algorithm. Using the developed system, we examined the ears of 69 patients to assess their performance for distinguishing between normal, OME, and AdOM ears. In the classification of ear diseases, the multimode system based on machine learning analysis performed better in terms of accuracy and F1 scores than single RGB image analysis, RGB/fluorescence image analysis, and the analysis of spectral image cubes only, respectively. These results demonstrate that the intelligent multimode diagnostic capability of an otoscope would be beneficial for better diagnosis and management of OM.

RGB images-based vegetative index for phenotyping kenaf (Hibiscus cannabinus L.).

Kenaf (Hibiscus cannabinus L.) is an industrial crop used as a raw material in various fields and is cultivated worldwide. Compared to high potential for its utilization, breeding sector is not vigorous partially due to laborous breeding procedure. Thus, efficient breeding methods are required for varieties that can adapt to various environments and obtain optimal production. For that, identifying kenaf’s characteristics is very important during the breeding process. Here, we investigated if RGB based vegetative index (VI) could be associated with traits for biomass. We used 20 varieties and germplasm of kenaf and RGB images taken with unmanned aerial vehicles (UAVs) for field selection in early and late growth stage. In addition, measuring the stem diameter and the number of nodes confirmed whether the vegetative index value obtained from the RGB image could infer the actual plant biomass. Based on the results, it was confirmed that the individual surface area and estimated plant height, which were identified from the RGB image, had positive correlations with the stem diameter and node number, which are actual growth indicators of the rate of growth further, biomass could also be estimated based on this. Moreover, it is suggested that VIs have a high correlation with actual growth indicators; thus, the biomass of kenaf could be predicted. Interstingly, those traits showing high correlation in the late stage had very low correlations in the early stage. To sum up, the results in the current study suggest a more efficient breeding method by reducing labor and resources required for breeding selection by the use of RGB image analysis obtained by UAV. This means that considerable high-quality research could be performed even with a tight budget. Furthermore, this method could be applied to crop management, which is done with other vegetative indices using a multispectral camera.

A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots.

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

Turmeric and RGB Analysis: A Low-Cost Experiment for Teaching Acid–Base Equilibria at Home

An experimental approach to chemistry is a more meaningful way to learn than the use of textbooks alone. Working in the laboratory helps students understand basic concepts through their active engagement. Here, we present a series of inexpensive and environmentally friendly laboratory exercises on acid–base equilibrium. Their difficulty level is low, and they can be performed at home. Thus, they are especially suited for teaching in a social-distance context, such as during the COVID-19 pandemic. The experiments were designed to exemplify the main concepts of acid–base equilibrium, buffer capacity, and titration curves, using safe household reactants and no special equipment. RGB image analysis is used to detect pH variation and follow experiments’ progress. Titration of sodium carbonate with acetic acid using curcumin and red cabbage extracts as indicators is described. Our results show that curcumin extract is an excellent indicator for this purpose, and the end point is clearly observed through image analysis. These assays can be used and further adapted to teach students of different ages and socioeconomic backgrounds.

Programmable actuator based on RGB monitoring for detection and dissociation of gas hydrates

Interest in gas hydrates has increased in recent years, both due to the high availability of fuel gases under hydrate form and the problems related to the formation of deposits in offshore pipelines during crude oil production. This paper aims to describe a new application of a real-time RGB image-analysis system to evaluate hydrate crystallization and to control the formation of deposits through the automated injection of chemical additives. Induction time values obtained by RGB image analysis were similar to calorimetry results, and both were more assertive when compared to torque measurements. Using carbon dioxide or methane as guest gaseous molecule, RGB image analysis was used to detect an increase on induction time with temperature, and a decrease on this parameter with increasing pressure or vessel size. For CO2 at 40 bar, induction time increased from 12.8 to 118.5 min when temperature was increased from 3 to 5 °C, and decreased from 198.8 to 6.0 min and pressure was raised from 30 to 50 bar at 3 °C. For CH4 at 40 bar, induction time increased from 260.2 to 516.9 min when temperature was increased from 1 to 3 °C, and decreased from 516.9 to 0.5 min and pressure was raised from 40 to 60 bar at 3 °C. The programmable actuator was able to respond to hydrate formation and inject a thermodynamic inhibitor (ethanol or monoethylene glycol) for the dissolution of the deposits approximately 10 min after formation.

UV sensor based on polyurethane foam

Ultraviolet light (UV) present in solar radiation has an important effect on the health of human skin. For that reason, we developed a disposable and easy-to-manufacture UV light sensor based on surface color change of a polyurethane (PU) film and examined its performance against radiation received from a lamp or from the sun. The chemical modification after UV exposition and the consequent color variation from light yellow to brown in PU sensor were studied by using RGB image analysis and a reflectance prototype containing an Arduino board. It was found that the photochemical modification of PU films increases absorption of visible radiation in the blue spectrum range. Furthermore, PU surface was more sensitive to UV (<390) than that found in visible light when exposed to sunlight, which was associated with the presence of a chromophore in the material modified. In addition, it was found that the extract obtained from irradiated PU showed its maximum absorption at 309 nm. Hence, the use of this economical light radiation sensor seems to be a very promising tool to determine exposure to UV light from the solar spectrum.

Association of PLGA Microspheres to Carrier Pellets by Fluid Bed Coating: A Novel Approach towards Improving the Flowability of Microparticles.

Micro- and nanoparticles have been vastly studied due to their biopharmaceutical advantages. However, these particles generally display very weak packing and poor mechanical properties. Hereby, a new methodology is proposed to associate poorly flowing particles to macrostructures targeting the improvement of flowability and redispersibility of the particles. Cecropia glaziovii-loaded PLGA microspheres (4.59 ± 0.04 μm) were associated with carrier pellets by film coating in a top-spray fluid bed equipment. Optimal conditions were determined employing a IV-Optimal factorial design and RGB image analysis as 1% (w/v) Kollicoat® Protect as coating polymer (2:1 weight ratio of coating suspension to carrier pellets), containing 5 mg/mL microspheres (loading of 28.07 ± 1.01 mg/g). The method led to an improvement of the overall flowability. No relevant molecular interactions between PLGA microspheres and polymers were found. Microspheres detached rapidly from the surface of the pellets, without agglomeration, when exposed to hydrodynamic forces. In vitro release profiles, prior to and after fluid bed coating, showed no relevant changes in drug release rate and extent. The methodology developed is suitable for further applications when an improvement on the flow properties and redispersibility of the product is desired. We showed an easy-to-implement methodology that can be executed without significant increase in costs.

A non-destructive technique for health assessment of fire-damaged concrete elements using terrestrial laser scanning

Concrete structures are routinely monitored to detect changes in their characteristics in the field of engineering surveying and other disciplines such as structural and civil engineering. There is growing demand for the development of reliable Non-Destructive Testing (NDT) techniques for concrete structures in the assessment of the deteriorating condition of infrastructures or in an event of fire-damaged structures. In this paper, the feasibility of using Terrestrial Laser Scanning (TLS) technology for change detection and assessment of fire-damaged concrete has been investigated through measurements and analysis of laboratory size concrete specimens that underwent heating up to 1000°C. The TLS technique employed in detecting fire-damaged concrete involved modelling and analysis of the TLS intensity returns as well as RGB image analysis. The results obtained clearly demonstrate the feasibility of using TLS to detect fire-damaged concrete. Although the laser scanners used in the study have different wavelengths, the results obtained in both cases are promising for a detection technique of fire-damaged concrete structures.