Dataset of chlorophyll content estimation of Coffea Canephora using Red and Near-Infrared consumer-grade camera.
Ontology highlight
ABSTRACT:
SUBMITTER: Widjaja Putra BT
PROVIDER: S-EPMC6214832 | biostudies-other | 2018 Dec
REPOSITORIES: biostudies-other
Publications
Dataset of chlorophyll content estimation of <i>Coffea Canephora</i> using Red and Near-Infrared consumer-grade camera.
Widjaja Putra Bayu Taruna BT Soni Peeyush P
Data in brief 20181016
This dataset presents a series of broad Red and Near-Infrared (NIR) bands obtained from consumer-grade camera in estimating chlorophyll contents in Robusta coffee (<i>Coffea Canephora</i>) plants at the leaf level. A total of 600 leaves were measured using direct-leaf tools namely SPAD-502 chlorophyll meter, Spectrometer, and modified consumer-grade camera using a 665 nm long-pass external filter. ...[more]
Similar Datasets
Project description:Near-infrared (NIR) fluorescence has attracted much attention in biomedical fields because it offers deep tissue penetration and high spatial resolution. Herein, a method is developed for the preparation of NIR fluorescent nanocomposites (NCs) by encapsulating natural chlorophyll (Chl) into the micelles of octylamine-modified poly(acrylic acid) (OPA). Both femtosecond transient absorption spectra and isothermal titration calorimetry thermogram reveal that the micelles of OPA provide a hydrophobic environment for the improved fluorescence efficiency. Hence the resulted Chl NCs possess unique properties such as ultrasmall size, outstanding photostability, good biocompatibility, and superbright NIR fluorescence emission. In vivo imaging of sentinel lymph node is achieved in nude mice, demonstrating the potential of Chl NCs in biomedical applications. This work provides a new strategy for the preparation of highly biocompatible NIR fluorescence labeling nanocomposites.
| S-EPMC7315411 | biostudies-literature
Project description:We need data sets of images and subjective scores to develop robust no reference (or blind) visual quality metrics for consumer applications. These applications have many uncontrolled variables because the camera creates the original media and the impairment simultaneously. We do not fully understand how this impacts the integrity of our subjective data. We put forward two new data sets of images from consumer cameras. The first data set, CCRIQ2, uses a strict experiment design, more suitable for camera performance evaluation. The second data set, VIME1, uses a loose experiment design that resembles the behavior of consumer photographers. We gather subjective scores through a subjective experiment with 24 participants using the Absolute Category Rating method. We make these two new data sets available royalty-free on the Consumer Digital Video Library. We also present their integrity analysis (proposing one new approach) and explore the possibility of combining CCRIQ2 with its legacy counterpart. We conclude that the loose experiment design yields unreliable data, despite adhering to international recommendations. This suggests that the classical subjective study design may not be suitable for studies using consumer content. Finally, we show that Hoßfeld-Schatz-Egger α failed to detect important differences between the two data sets.
| S-EPMC8321034 | biostudies-literature
Project description:Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are primarily determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. This data article aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm-1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes n = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for n = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.
| S-EPMC10051025 | biostudies-literature
Project description:This study was conducted to evaluate the feasibility of using near-infrared spectroscopy (NIRS) to predict beef consumers' perceptions. Photographs of 200 raw steaks were taken, and NIRS data were collected (transmittance and reflectance). The steak photographs were used to conduct a face-to-face survey of 400 beef consumers. Consumers rated beef color, visible fat, and overall appearance, using a 5-point Likert scale (where 1 indicated "Dislike very much" and 5 indicated "Like very much"), which later was simplified in a 3-point Likert scale. Factor analysis and structural equation modeling (SEM) were used to generate a beef consumer index. A partial least square discriminant analysis (PLS-DA) was used to predict beef consumers' perceptions using NIRS data. SEM was used to validate the index, with root mean square errors of approximation ?0.1 and comparative fit and Tucker-Lewis index values <0.9. PLS-DA results for the 5-point Likert scale showed low prediction (accuracy < 42%). A simplified 3-point Likert scale improved discrimination (accuracy between 52% and 55%). The PLS-DA model for purchasing decisions showed acceptable prediction results, particularly for transmittance NIRS (accuracy of 76%). Anticipating beef consumers' willingness to purchase could allow the beef industry to improve products so that they meet consumers' preferences.
| S-EPMC7466230 | biostudies-literature
Project description:Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage (R2 = 0.35-0.71) and growth rates in early canopy development stages (R2 = 0.29-0.52). Multi-dimensional traits (e.g., projected canopy area and volume) outperformed one-dimensional traits, and the multivariate trait (cumulative height profile) outperformed univariate traits. The proposed approach would be useful for identification of quantitative trait loci (QTLs) controlling canopy size in genetics/genomics studies or for fiber yield prediction in breeding programs and production environments.
| S-EPMC5797632 | biostudies-literature
Project description:The term "hyperscanning" refers to the simultaneous recording of multiple individuals' brain activity. As a methodology, hyperscanning allows the investigation of brain-to-brain synchrony. Despite being a promising technique, there is a limited number of publicly available functional Near-infrared Spectroscopy (fNIRS) hyperscanning recordings. In this paper, we report a dataset of fNIRS recordings from the prefrontal cortical (PFC) activity of 33 mother-child dyads and 29 father-child dyads. Data was recorded while the parent-child dyads participated in an experiment with two sessions: a passive video attention task and a free play session. Dyadic metadata, parental psychological traits, behavioural annotations of the play sessions and information about the video stimuli complementing the dataset of fNIRS signals are described. The dataset presented here can be used to design, implement, and test novel fNIRS analysis techniques, new hyperscanning analysis tools, as well as investigate the PFC activity in participants of different ages when they engage in passive viewing tasks and active interactive tasks.
| S-EPMC9569382 | biostudies-literature
Project description:Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quantification relies on non-destructive near-infrared (NIR) spectroscopy which is not invasive and can be performed on-farm, in real-time. The current dataset contains NIR spectral measurements in transmittance mode in the wavelength range from 960 nm to 1690 nm of 1224 individual raw milk samples, collected on-farm over an eight-week span in 2017, at the experimental dairy farm of the province of Antwerp, 'Hooibeekhoeve' (Geel, Belgium). For these spectral measurements, laboratory reference values corresponding to the three main components of raw milk (fat, protein and lactose), urea and somatic cell count (SCC) are included. This data has been used to build multivariate calibration models to predict the three milk compounds, as well as develop strategies to monitor the prediction performance of the calibration models.
| S-EPMC10700509 | biostudies-literature
Project description:Extra virgin olive oil (EVOO) is very appreciated by its taste, flavor, and benefits for health, and so, it has a high price of commercialization. This fact makes it necessary to provide reliable and cost-effective analytical procedures, such as near-infrared (NIR) spectroscopy, to analyze its traceability and purity, in combination with chemometrics. Fatty acids profile of EVOO, considered as a quality parameter, is estimated, firstly, from NIR data and, secondly, by adding agro-climatic information. NIR and agro-climatic data sets are summarized by using principal component analysis (PCA) and treated by both scalar and functional approaches. The corresponding PCA and FPCA are progressively introduced in regression models, whose goodness of fit is evaluated by the dimensionless root-mean-square error. In general, SFAs, MUFAs, and PUFAs (and disaggregated fatty acids) estimations are improved by adding agro-climatic besides NIR information (mainly, temperature or evapotranspiration) and considering a functional point of view for both NIR and agro-climatic data.
| S-EPMC6977507 | biostudies-literature
Project description:We developed a single-camera-based near-infrared (NIR) fluorescence imaging device using indocyanine green (ICG) NIR fluorescence contrast agents for image-induced surgery. In general, a fluorescent imaging system that simultaneously provides color and NIR images uses two cameras, which is disadvantageous because it increases the imaging head of the system. Recently, a single-camera-based NIR optical imaging device with quantum efficiency partially extended to the NIR region was developed to overcome this drawback. The system used RGB_NIR filters for camera sensors to provide color and NIR images simultaneously; however, the sensitivity and resolution of the infrared images are reduced by 1/4, and the exposure time and gain cannot be set individually when acquiring color and NIR images. Thus, to overcome these shortcomings, this study developed a compact fluorescent imaging system that uses a single camera with two complementary metal-oxide semiconductor (CMOS) image sensors. Sensitivity and signal-to-background ratio were measured according to the concentrations of ICG solution, exposure time, and camera gain to evaluate the performance of the imaging system. Consequently, the clinical applicability of the system was confirmed through the toxicity analysis of the light source and in vivo testing.
| S-EPMC9370963 | biostudies-literature
Project description:Near-infrared photoimmunotherapy (NIR-PIT) is a new type of cancer treatment, which was recently approved in Japan for patients with inoperable head and neck cancer. NIR-PIT utilizes antibody-IRDye700DX (IR700) conjugates and NIR light at a wavelength of 690 nm. NIR light exposure leads to physicochemical changes in the antibody-IR700 conjugate cell receptor complex, inducing rapid necrotic cell death. Just as fluorescence guided surgery is useful for surgeons to resect tumors completely, real-time information of tumor locations would help clinicians irradiate NIR light more precisely. IR700 is a fluorescence dye that emits at 702 nm; however, there is no clinically available device optimized for detecting this fluorescence. On the other hand, many indocyanine green (ICG) fluorescence imaging devices have been approved for clinical use. Therefore, we investigated whether LIGHTVISION, one of the clinically available ICG cameras, could be employed for tumor detection. We hypothesized that irradiation with even low-power 690-nm laser light, attenuated by 99% with a neutral-density filter, could be detected with LIGHTVISION without fluorescence decay or therapeutic effect because of the long emission tail of IR700 beyond 800 nm (within the detection range of LIGHTVISION). We demonstrated that the LIGHTVISION camera, originally designed for ICG detection, can detect the tail of IR700 fluorescence in real time, thus enabling the visualization of target tumors.
| S-EPMC7935778 | biostudies-literature