scholarly journals Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

2021 ◽  
Vol 13 (21) ◽  
pp. 4489
Author(s):  
Robert Chancia ◽  
Terry Bates ◽  
Justine Vanden Heuvel ◽  
Jan van Aardt
Keyword(s):  
Feature Selection ◽  
Spectral Data ◽  
Multispectral Imaging ◽  
Hyperspectral Imagery ◽  
Nutrient Status ◽  
Unmanned Aerial Systems ◽  
Nutrient Contents ◽  
Spectral Bands ◽  
Trade Offs ◽  
Leave One Out

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra.

scholarly journals Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study

2021 ◽  
Vol 13 (2) ◽  
pp. 211
Author(s):  
Maële Brisset ◽  
Simon Van Wynsberge ◽  
Serge Andréfouët ◽  
Claude Payri ◽  
Benoît Soulard ◽  
...  
Keyword(s):  
Green Algae ◽  
Algal Blooms ◽  
New Caledonia ◽  
Spectral Indices ◽  
Green Macroalgae ◽  
Spectral Bands ◽  
Algae Blooms ◽  
Trade Offs ◽  
Sentinel 2

Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons.

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data

2021 ◽  
Author(s):  
Zsófia Adrienn Kovács ◽  
János Mészáros ◽  
Mátyás Árvai ◽  
Annamária Laborczi ◽  
Gábor Szatmári ◽  
...  
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Hyperspectral Imagery ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Soil Carbon Content ◽  
Space Agency ◽  
Italian Space Agency ◽  
Agricultural Areas ◽  
Imagery Data

<p>The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.</p><p>The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.</p><p>The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.</p><p>Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km<sup>2</sup> in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.</p><p>We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.</p><p>For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.</p><p> </p><p><strong>Acknowledgment:</strong> Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022). Our project carried out using PRISMA Products, © of the Italian Space Agency (ASI), delivered under an ASI License to use.</p>

VIS-NIR hyperspectral cameras

Photoniques ◽  
2021 ◽  
pp. 58-64
Author(s):  
Stéphane Tisserand
Keyword(s):  
Multispectral Imaging ◽  
Three Dimensional ◽  
Spectral Domain ◽  
Spectral Bands ◽  
Cmos Sensors

Hyperspectral and multispectral imaging can record a single scene across a range of spectral bands. The resulting three-dimensional dataset is called a "hypercube". A spectrum is available for each point of the image. This makes it possible to analyse, quantify or differentiate the elements and materials constituting the scene. This article presents the existing technologies on the market and their main characteristics in the VIS/NIR spectral domain (400-1000 nm). It then focuses on a specific multispectral technology called snapshot multispectral imaging, combining CMOS sensors and pixelated multispectral filters (filtering at the pixel level).

scholarly journals Informative Gene Selection and Direct Classification of Tumor Based on Chi-Square Test of Pairwise Gene Interactions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hongyan Zhang ◽  
Lanzhi Li ◽  
Chao Luo ◽  
Congwei Sun ◽  
Yuan Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
Gene Interactions ◽  
Selection Methods ◽  
Chi Square ◽  
Generalization Performance ◽  
Independent Test ◽  
Chi Square Test ◽  
Leave One Out ◽  
Tumor Gene

In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is useful to mine profiles for informative genes with definite biological meanings and to build robust classifiers with high precision. In this study, we developed a new method for tumor-gene selection, the Chi-square test-based integrated rank gene and direct classifier (χ2-IRG-DC). First, we obtained the weighted integrated rank of gene importance from chi-square tests of single and pairwise gene interactions. Then, we sequentially introduced the ranked genes and removed redundant genes by using leave-one-out cross-validation of the chi-square test-based Direct Classifier (χ2-DC) within the training set to obtain informative genes. Finally, we determined the accuracy of independent test data by utilizing the genes obtained above withχ2-DC. Furthermore, we analyzed the robustness ofχ2-IRG-DC by comparing the generalization performance of different models, the efficiency of different feature-selection methods, and the accuracy of different classifiers. An independent test of ten multiclass tumor gene-expression datasets showed thatχ2-IRG-DC could efficiently control overfitting and had higher generalization performance. The informative genes selected byχ2-IRG-DC could dramatically improve the independent test precision of other classifiers; meanwhile, the informative genes selected by other feature selection methods also had good performance inχ2-DC.

scholarly journals FEATURE FILTERING AND SELECTION FOR DRY MATTER ESTIMATION ON PERENNIAL RYEGRASS: A CASE STUDY OF VEGETATION INDICES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1827-1831 ◽  
Author(s):  
G. T. Alckmin ◽  
L. Kooistra ◽  
A. Lucieer ◽  
R. Rawnsley
Keyword(s):  
Feature Selection ◽  
Perennial Ryegrass ◽  
Dry Matter ◽  
Selection Process ◽  
Vegetation Indices ◽  
Robust Model ◽  
Trade Offs ◽  
Initial Dataset ◽  
High Degree

<p><strong>Abstract.</strong> Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n&amp;thinsp;=&amp;thinsp;900), indicates that for this response variable (i.e. kg.DM.ha<sup>&amp;minus;1</sup>), more than 80% of indices present a high degree of collinearity (correlation&amp;thinsp;&amp;gt;&amp;thinsp;|0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error &amp;ndash; RMSE&amp;thinsp;=&amp;thinsp;412.27&amp;thinsp;kg.DM.ha<sup>&amp;minus;1</sup>) and tolerable models (with a smaller number of features &amp;ndash; 4 VIs and within 10% of the lowest RMSE.)</p>

Detecting Levels and Innovative Applications for the Detection of Aromatic Compounds Using Multivariate Curve Analysis and Spectroscopy Data

2021 ◽  
Vol 19 (3) ◽  
pp. 46-55
Author(s):  
Al-Ibadi Zeyad ◽  
Muthana Alboedam ◽  
Ilya Katanov ◽  
Al-Zubaidi Sura
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Aromatic Compounds ◽  
Curve Analysis ◽  
Spectral Bands ◽  
Technological Developments ◽  
Benzene Toluene ◽  
Technical Solutions ◽  
Spectral Mixture ◽  
Specific Degree

Rapid technological developments and the increasing complexity of matrix work are increasing interest in finding robust technical solutions and fast and assertive data analysis. The data can be filtered to obtain spectral data in more useful spectral bands, the developed algorithms allow quantification of the spectral mixture, and it can be measured with or without titration. We focused in this study is on samples within the range of wavelengths (459,466,462,464 nm), different strategies are utilized to check Aromatic compounds. These enable us to survey a specific degree of ordinary compounds, much the same as benzene, toluene and xylene, and so on. In this study compares data analysis, received from sensor between A polynomial approximation PolyFit method and Processors Gases method, and compares the results of each, a method with the multivariate curve resolve - alternate least squares (MCR - ALS) regression strategy of analyzing spectral data of information with a different concentration. Additionally, adequately moo deviations of the anticipated values were accomplished from the genuine values, the standard deviation. And, the amended range was normalized to their region and to some degrees smooth. The autofluorescence establishment was subtracted, for the pure extend investigation, by utilizing logical approaches.

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