Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the early detection of apple scab disease.
Ontology highlight
ABSTRACT:
SUBMITTER: Nouri M
PROVIDER: S-EPMC5752091 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.
| S-EPMC6068824 | biostudies-other
Project description:Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe ("myocommata") and red muscle ("myotome") pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.
| S-EPMC7570506 | biostudies-literature
Project description:A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were performed on healthy leaves and leaves with foliar symptoms caused by different grapevine diseases showing clear symptoms of biotic or abiotic stress on other organs. For each leaf, chemical measurements such as chlorophyll and flavonol contents were also performed.
| S-EPMC10603033 | biostudies-literature
Project description:BACKGROUND:Tracking newly emergent virulent populations in agroecosystems provides an opportunity to increase our understanding of the co-evolution dynamics of pathogens and their hosts. On the one hand host plants exert selective pressure on pathogen populations, thus dividing them into subpopulations of different virulence, while on the other hand they create an opportunity for secondary contact between the two divergent populations on one tree. The main objectives of the study were to explore whether the previously reported structure between two Venturia inaequalis population types, virulent or avirulent towards Malus x domestica cultivars carrying Rvi6 gene, is maintained or broken several years after the first emergence of new virulent strains in Poland, and to investigate the relationship between 'new' and 'native' populations derived from the same commercial orchards. For this purpose, we investigated the genetic structure of populations of the apple scab fungus, occurring on apple tree cultivars containing Rvi6, Rvi1 or Rvi17 resistance gene or no resistance at all, based on microsatellite data obtained from 606 strains sampled in 10 orchards composed of various host cultivars. RESULTS:Application of genetic distance inferring and clustering methods allowed us to observe clear genetic distinctness of the populations virulent towards cultivars carrying Rvi6 gene from the Rvi6-avirulent populations and substructures within the Rvi6-group as a consequence of independent immigration events followed by rare, long-distance dispersals. We did not observe such a structuring effect of other genes determining apple scab resistance on any other populations, which in turn were genetically homogenous. However, in two orchards the co-occurrence of strains of different virulence pattern on the same trees was detected, blurring the genetic boundaries between populations. CONCLUSIONS:Among several resistance genes studied, only Rvi6 exerted selective pressure on pathogens populations: those virulent toward Rvi6 hosts show unique and clear genetic and virulence pattern. For the first time in commercial Malus x domestica orchards, we reported secondary contacts between populations virulent and avirulent toward Rvi6 hosts. These two populations, first diverged in allopatry, second came into contact and subsequently began interbreeding, in such way that they show unambiguous footprints of gene flow today.
| S-EPMC5775622 | biostudies-literature
Project description:PremiseBlack Sigatoka is one of the most severe banana (Musa spp.) diseases worldwide, but no methods for the rapid early detection of this disease have been reported. This paper assesses the use of hyperspectral images for the development of a partial-least-squares penalized-logistic-regression (PLS-PLR) model and a hyperspectral biplot (HS biplot) as a visual tool for detecting the early stages of black Sigatoka disease.MethodsYoung (three-month-old) banana plants were inoculated with a conidia suspension of the black Sigatoka fungus (Pseudocercospora fijiensis). Selected infected and control plants were evaluated using a hyperspectral imaging system at wavelengths in the range of 386-1019 nm. PLS-PLR models were run on the hyperspectral data set. The prediction power was assessed using leave-one-out cross-validation as well as external validation.ResultsThe PLS-PLR model was able to predict the presence of the disease with a 98% accuracy. The wavelengths with the highest contribution to the classification ranged from 577 to 651 nm and from 700 to 1019 nm.DiscussionPLS-PLR and HS biplot effectively estimated the presence of black Sigatoka disease at the early stages and can be used to graphically represent the relationship between groups of leaves and both visible and near-infrared wavelengths.
| S-EPMC7507400 | biostudies-literature
Project description:Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874?1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.
| S-EPMC6021935 | biostudies-other
Project description:The intrinsic near-infrared photoluminescence (fluorescence) of single-walled carbon nanotubes exhibits unique photostability, narrow bandwidth, penetration through biological media, environmental sensitivity, and both chromatic variety and range. Biomedical applications exploiting this large family of fluorophores will require the spectral and spatial resolution of individual (n,m) nanotube species' fluorescence and its modulation within live cells and tissues, which is not possible with current microscopy methods. We present a wide-field hyperspectral approach to spatially delineate and spectroscopically measure single nanotube fluorescence in living systems. This approach resolved up to 17 distinct (n,m) species (chiralities) with single nanotube spatial resolution in live mammalian cells, murine tissues ex vivo, and zebrafish endothelium in vivo. We anticipate that this approach will facilitate multiplexed nanotube imaging in biomedical applications while enabling deep-tissue optical penetration, and single-molecule resolution in vivo.
| S-EPMC4585673 | biostudies-literature
Project description:This article presents the capture protocol to acquire hyperspectral images, which can be used to quantify the concentration of total phosphorus in soil samples. 152 soil samples were prepared, and a hyperspectral cube made up of 145 images in the VIS-NIR bands, between 420 and 1000 nm, was obtained from each of them. The images obtained were taken with the Bayspec OCIF Series hyperspectral camera, in push-broom function, using a platform that includes an illumination system that offers a continuous spectrum in the range of interest. The samples were prepared with a soil from the Santander de Quilichao region, Cauca, Colombia, and mixed with known concentrations of P2O5 fertilizer, so that a total mass of 50 g was obtained. Each sample was deposited in a round black plastic container, 6 cm in diameter and a depth of 1 cm. The soil samples were analyzed in the laboratory to establish the concentration of total phosphorus. Therefore, the database is made up of the images associated with the hyperspectral cube of each sample, and four tables: the first describes the properties of the soil used to obtain the mixtures, the second the composition of the fertilizer used, the third describes the soil-fertilizer ratio to make up the samples, and the fourth was the laboratory analysis of the total phosphorus content of the analyzed samples.
| S-EPMC9730032 | 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