scholarly journals Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yan Zhao ◽  
Bangyou Zheng ◽  
Scott C. Chapman ◽  
Kenneth Laws ◽  
Barbara George-Jaeggli ◽  
...  
Keyword(s):  
Spectral Band ◽  
Band Alignment ◽  
Learning Approaches ◽  
Discriminative Ability ◽  
Spectral Bands ◽  
Diverse Field ◽  
Sorghum Plant ◽  
High Throughput Phenotyping ◽  
Uav Images ◽  
Reflectance Data

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.

scholarly journals High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

2021 ◽  
Author(s):  
Marcus Vinicius Vieira Borges ◽  
Janielle de Oliveira Garcia ◽  
Tays Silva Batista ◽  
Alexsandra Nogueira Martins Silva ◽  
Fabio Henrique Rojo Baio ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Plant Size ◽  
Eucalyptus Species ◽  
Forest Inventories ◽  
Spectral Bands ◽  
High Throughput Phenotyping ◽  
Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

scholarly journals TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

2016 ◽  
Vol 22 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Eder Paulo Moreira* ◽  
Márcio de Morisson Valeriano ◽  
Ieda Del Arco Sanches ◽  
Antonio Roberto Formaggio
Keyword(s):  
Near Infrared ◽  
Incidence Angle ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Topographic Effect ◽  
Topographic Correction ◽  
Spectral Bands ◽  
Spectral Vegetation Indices ◽  
Current Availability ◽  
Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

scholarly journals Radiative Properties and Numerical Modeling of C4F7N-CO2-O2 Thermal Plasma

2019 ◽  
Vol 6 (2) ◽  
pp. 144-147
Author(s):  
M. Gnybida ◽  
Ch. Ruempler ◽  
V. R. T. Narayanan
Keyword(s):  
Thermal Plasma ◽  
Spectral Band ◽  
Optimization Procedure ◽  
Optimal Number ◽  
Radiative Properties ◽  
Absorption Coefficients ◽  
Medium Voltage ◽  
Spectral Bands ◽  
Computational Fluid Dynamics Cfd ◽  
Low Global Warming Potential

C<sub>4</sub>F<sub>7</sub>N and C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> mixtures are considered as alternatives to SF<sub>6</sub> for use in medium voltage gas insulated switchgear applications (GIS), due to the low global warming potential and good dielectric properties of C<sub>4</sub>F<sub>7</sub>N. Current work is focused on the calculation of radiative properties (absorption coefficients) of C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> thermal plasma and computational fluid dynamics (CFD) simulations of free burning C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> arcs that are stabilized by natural convection. Absorption coefficients of C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> plasma used in the CFD model are derived from spectral absorption coefficients by Planck averaging. An optimization procedure has been applied to find the optimal number of spectral bands as well as spectral band interval boundaries. Radiation and flow model results for C<sub>4</sub>F<sub>7</sub>N-CO<sub>2</sub> in comparison to SF<sub>6</sub> and air are provided and discussed.

Differentiation of turfgrass and common weed species using hyperspectral radiometry

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 335-339 ◽  
Author(s):  
Kendall C. Hutto ◽  
David R. Shaw ◽  
John D. Byrd ◽  
Roger L. King
Keyword(s):  
Data Collection ◽  
Classification Accuracy ◽  
Spectral Characteristics ◽  
Creeping Bentgrass ◽  
Hyperspectral Reflectance ◽  
Weed Species ◽  
Spectral Bands ◽  
Virginia Buttonweed ◽  
Reflectance Data

Hand-held hyperspectral reflectance data were collected in the summers of 2002, 2003, and 2004 to differentiate unique spectral characteristics of common turfgrass and weed species. Turfgrass species evaluated were: bermudagrass, ‘Tifway 419’; zoysiagrass, ‘Meyer’; St. Augustinegrass, ‘Raleigh’; common centipedegrass; and creeping bentgrass, ‘Crenshaw’. Weed species evaluated were: dallisgrass, southern crabgrass, eclipta, and Virginia buttonweed. Reflectance data were collected from greenhouse and field locations. An overall classification accuracy of 85% was achieved for all species in the field. A total of 21 spectral bands between 378 and 1,000 nm that were consistent over the three data collection periods were used for analysis. Only centipedegrass, zoysiagrass, and dallisgrass were correctly classified less than 80% of the time. An overall classification accuracy of 69% was achieved for the greenhouse species. Spectral bands used in this analysis ranged from 353 to 799 nm. Creeping bentgrass and Virginia buttonweed were classified correctly at 96 and 92%, respectively.

scholarly journals Covering Graphs, Magnetic Spectral Gaps and Applications to Polymers and Nanoribbons

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1163 ◽  
Author(s):  
John Stewart Fabila-Carrasco ◽  
Fernando Lledó
Keyword(s):  
Magnetic Field ◽  
Control Parameter ◽  
Spectral Band ◽  
Sufficient Conditions ◽  
Magnetic Potential ◽  
Spectral Gaps ◽  
Spectral Bands ◽  
Covering Graph ◽  
Abelian Lattice ◽  
Band Gap Structure

In this article, we analyze the spectrum of discrete magnetic Laplacians (DML) on an infinite covering graph G ˜ → G = G ˜ / Γ with (Abelian) lattice group Γ and periodic magnetic potential β ˜ . We give sufficient conditions for the existence of spectral gaps in the spectrum of the DML and study how these depend on β ˜ . The magnetic potential can be interpreted as a control parameter for the spectral bands and gaps. We apply these results to describe the spectral band/gap structure of polymers (polyacetylene) and nanoribbons in the presence of a constant magnetic field.

scholarly journals Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

2020 ◽  
Vol 12 (8) ◽  
pp. 1238 ◽  
Author(s):  
Andrew Fletcher ◽  
Richard Mather
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Spectral Band ◽  
Point Clouds ◽  
Tree Canopy ◽  
Solar Elevation ◽  
Spectral Bands ◽  
Eucalyptus Forest ◽  
Dense Point ◽  
Solar Noon

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

The relationship of εL and εR from consideration of bisignate C.D. curves of α,β-unsaturated ketones

1984 ◽  
Vol 37 (4) ◽  
pp. 667 ◽  
Author(s):  
AF Beecham ◽  
W Fock ◽  
AM Mathieson
Keyword(s):  
Experimental Evidence ◽  
Opposite Sign ◽  
Spectral Band ◽  
Alternative Interpretation ◽  
Constant Amplitude ◽  
Unsaturated Ketones ◽  
Spectral Bands ◽  
Single Transition ◽  
Relationship Of ◽  
The Relationship

From reconsideration of experimental evidence of ∆ε and ε curves and extrapolation of comments by Beecham and Collins1 it is concluded that the occurrence of bisignate n- π* c.d. curves for ketones and α,β-unsaturated ketones does not require hypothesizing the existence of two spectral bands of opposite sign and therefore indicates that the relationship between εL and εR for a single transition does not necessarily involve a simple constant amplitude factor as the conventional interpretation holds. An alternative interpretation of bisignate curves is proposed, based on one spectral band, the relationship between εL and εR involving differential half-widths of the vibronic components of the L series relative to the R series and a variable relative-amplitude scale along the vibronic progression. If appropriate scale and differential half-width parameters are used, the characteristics of monosignate and bisignate curves can be demonstrated by means of synthetic curves.

scholarly journals In-orbit Earth reflectance validation of TROPOMI on board the Sentinel-5 Precursor satellite

2020 ◽  
Author(s):  
Lieuwe G. Tilstra ◽  
Martin de Graaf ◽  
Ping Wang ◽  
Piet Stammes
Keyword(s):  
Radiative Transfer ◽  
Spectral Band ◽  
Limiting Factor ◽  
Real Surface ◽  
Radiometric Calibration ◽  
Wavelength Band ◽  
Surface Reflection ◽  
Spectral Bands ◽  
The Earth ◽  
Maximal Deviation

Abstract. The goal of the study described in this paper is to determine the accuracy of the radiometric calibration of the TROPOMI instrument in-flight, using its Earth radiance and solar irradiance measurements, from which the Earth reflectance is determined. The Earth reflectances are compared to radiative transfer calculations. We restrict ourselves to clear-sky observations as these are less difficult to model than observations containing clouds and/or aerosols. The limiting factor in the radiative transfer calculations is then the knowledge of the surface reflectance. We use OMI and SCIAMACHY surface Lambertian-equivalent reflectivity (LER) information to model the reflectivity of the Earth's surface. This Lambertian, non-directional description of the surface reflection contribution results in a relatively large source of uncertainty in the calculations. These errors can be reduced significantly by filtering out geometries for which we know that surface LER is a poor approximation of the real surface reflectivity. This filtering is done by comparing the OMI/SCIAMACHY surface LER information to MODIS surface BRDF information. We report calibration accuracies and errors for 21 selected wavelength bands between 328 and 2314 nm, located in TROPOMI spectral bands 3–7. All wavelength bands show good linear response to the intensity of the radiation and negligible offset problems. Reflectances in spectral bands 5 and 6 (wavelength bands 670 to 772 nm) have a good absolute agreement with the simulations, showing calibration errors on the order of 0.01 or 0–3 %. Trends over the mission lifetime, due to instrument degradation, are studied and found to be negligible at these wavelengths. Reflectances in bands 3 and 4 (wavelength bands 328 to 494 nm), on the other hand, are found to be affected by serious calibration errors, on the order of 0.004–0.02 and ranging between 6 % and 10 %, depending on the wavelength. The TROPOMI requirements (of 2 % maximal deviation) are not met in this case. Trends due to instrument degradation are also found, being strongest for the 328-nm wavelength band, and almost absent for the 494-nm wavelength band. The validation results obtained for TROPOMI spectral band 7 show behaviour that we cannot fully explain. As a result, these results call for more research and different methods to study the calibration of the reflectance. It seems plausible, though, that the reflectance for this particular band is underestimated by about 6 %. A table is provided containing the final results for all 21 selected wavelength bands.

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