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:This dataset consists of three groups of hyperspectral images of apple tree plants. The first group of images consists of a temporal monitoring of seven apple tree plants, infected with fire blight (Erwinia amylovora), and six control plants over a period of 15 days. The second group of images includes a temporal monitoring of three infected plants, seven plants subjected to water stress, and seven control plants. The third group of images corresponds to acquisitions made in the orchard on nine trees showing symptoms of fire blight and six control trees. The pixel locations of infected areas have been provided for all images featuring symptomatic plants.
| S-EPMC10477057 | biostudies-literature
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:This article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478-901 nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0 to 9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written withdifferent pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 gs and manufactured by "AA" company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University & Research, the Netherlands.
| S-EPMC8873541 | biostudies-literature
Project description:BackgroundTracking 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.ResultsApplication 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.ConclusionsAmong 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:Endophytic fungi are microorganisms with the ability to colonize plants for the entire or at least a significant part of their life cycle asymptomatically, establishing a plant-fungus association. They play an important role in balancing ecosystems, as well as benefiting host through increasing plant growth, and protecting the host plants from abiotic and biotic stresses using various strategies. In the present study, endophytic fungi were isolated from wild and endemic apple cultivars, followed by characterizing their antifungal effect against Venturia inaequalis. To characterize the endophytic fungi, 417 fungal strains were separated from 210 healthy fruit, leaf, and branch samples collected from the north of Iran. Among the purified fungal isolates, 33 fungal genera were identified based on the morphological characteristics, of which 38 species were detected according to the morphological features and molecular data of ITS, tef-1α, and gapdh genomic regions (related to the genus). The results represented that most of the endophytic fungi belonged to Ascomycota (67.8%), 31.4% of isolates were mycelia sterilia, while the others were Basidiomycota (0.48%) and Mucoromycota (0.24%). Additionally, Alternaria, Cladosporium, and Nigrospora were determined as the dominant genera. The antifungal properties of the identified isolates were evaluated against V. inaequalis in vitro to determine the release of media-permeable metabolites, Volatile Organic Compounds (VOCs), chitinase, and cellulase as antifungal mechanisms, as well as producing phosphate solubilisation as growth-promoting effect. Based on the results of metabolite and VOC tests, the six isolates of Acremonium sclerotigenum GO13S1, Coniochaeta endophytica 55S2, Fusarium lateritium 61S2, Aureobasidium microstictum 7F2, Chaetomium globosum 2S1 and Ch. globosum 3 L2 were selected for greenhouse tests. Further, Co. endophytica 55S2 and F. lateritium 61S2 could solubilize inorganic phosphate. All isolates except Ch. globosum 3 L2 exhibited cellulase activity, while chitinase activity was observed in Ch. globosum 2S1, Ch. globosum 3 L2, and F. lateritium 61S2. Finally, Co. endophytica 55S2 and Ch. globosum 2S1 completely controlled the disease on the apple seedling leaves under greenhouse conditions.
| S-EPMC9677113 | biostudies-literature