scholarly journals Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms

2018 ◽  
Vol 4 (12) ◽  
pp. 143 ◽  
Author(s):  
Jan Behmann ◽  
David Bohnenkamp ◽  
Stefan Paulus ◽  
Anne-Katrin Mahlein
Keyword(s):  
Time Series ◽  
Plant Disease ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Reference Points ◽  
Transformation Model ◽  
Hyperspectral Images ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Disease Symptoms

The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.

scholarly journals Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images

2021 ◽  
Vol 129 ◽  
pp. 107985
Author(s):  
Yue Zhang ◽  
Chenzhen Xia ◽  
Xingyu Zhang ◽  
Xianhe Cheng ◽  
Guozhong Feng ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Hyperspectral Images ◽  
Crop Height

scholarly journals QTL Mapping of Seedling and Adult Plant Resistance to Septoria Tritici Blotch in Winter Wheat cv. Mandub (Triticum aestivum L.)

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1108
Author(s):  
Dominika Piaskowska ◽  
Urszula Piechota ◽  
Magdalena Radecka-Janusik ◽  
Paweł Czembor
Keyword(s):  
Mapping Population ◽  
Triticum Aestivum L ◽  
Seedling Stage ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Adult Plant ◽  
Resistance Locus ◽  
Phenotypic Variance ◽  
Breeding Programs ◽  
Plant Stage

Septoria tritici blotch (STB) is one of the most devastating foliar diseases of wheat worldwide. Host resistance is the most economical and safest method of controlling the disease, and information on resistance loci is crucial for effective breeding for resistance programs. In this study we used a mapping population consisting of 126 doubled-haploid lines developed from a cross between the resistant cultivar Mandub and the susceptible cultivar Begra. Three monopycnidiospore isolates of Z. tritici with diverse pathogenicity were used to test the mapping population and parents’ STB resistance at the seedling stage (under a controlled environment) and adult plant stage (polytunnel). For both types of environments, the percentage leaf area covered by necrosis (NEC) and pycnidia (PYC) was determined. A linkage map comprising 5899 DArTSNP and silicoDArT markers was used for the quantitative trait loci (QTL) analysis. The analysis showed five resistance loci on chromosomes 1B, 2B and 5B, four of which were derived from cv. Mandub. The location of QTL detected in our study on chromosomes 1B and 5B may suggest a possible identity or close linkage with Stb2/Stb11/StbWW and Stb1 loci, respectively. QStb.ihar-2B.4 and QStb.ihar-2B.5 detected on chromosome 2B do not co-localize with any known Stb genes. QStb.ihar-2B.4 seems to be a new resistance locus with a moderate effect (explaining 29.3% of NEC and 31.4% of PYC), conferring resistance at the seedling stage. The phenotypic variance explained by QTL detected in cv. Mandub ranged from 11.9% to 70.0%, thus proving that it is a good STB resistance source and can potentially be utilized in breeding programs.

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

scholarly journals Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa

2010 ◽  
Vol 67 (6) ◽  
pp. 1185-1197 ◽  
Author(s):  
C. Fernández ◽  
S. Cerviño ◽  
N. Pérez ◽  
E. Jardim
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Marine Science ◽  
Fishing Mortality ◽  
Change Over Time ◽  
Age Structured ◽  
Available Information ◽  
Over Time

Abstract Fernández, C., Cerviño, S., Pérez, N., and Jardim, E. 2010. Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa. – ICES Journal of Marine Science, 67: 1185–1197. A Bayesian age-structured stock assessment model is developed to take into account available information on discards and to handle gaps in the time-series of discard estimates. The model incorporates mortality attributable to discarding, and appropriate assumptions about how this mortality may change over time are made. The result is a stock assessment that accounts for information on discards while, at the same time, producing a complete time-series of discard estimates. The method is applied to the hake stock in ICES Divisions VIIIc and IXa, for which the available data indicate that some 60% of the individuals caught are discarded. The stock is fished by Spain and Portugal, and for each country, there are discard estimates for recent years only. Moreover, the years for which Portuguese estimates are available are only a subset of those with Spanish estimates. Two runs of the model are performed; one assuming zero discards and another incorporating discards. When discards are incorporated, estimated recruitment and fishing mortality for young (discarded) ages increase, resulting in lower values of the biological reference points Fmax and F0.1 and, generally, more optimistic future stock trajectories under F-reduction scenarios.

scholarly journals Quirky patterns in time-series of estimates of recruitment could be artefacts

2014 ◽  
Vol 72 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Dickey-Collas ◽  
N. T. Hintzen ◽  
R. D. M. Nash ◽  
P-J. Schön ◽  
M. R. Payne
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Reference Points ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Modelling Assumptions ◽  
Meta Analyses ◽  
Recruitment Model ◽  
Recruitment Time

Abstract The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model, and non-parametric “free” estimation of recruitment. We show that the recruitment time-series is sensitive to model assumptions and this can impact reference points in management, the perception of variability in recruitment and thus undermine meta-analyses. The assumption of the direct comparability of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena. This is especially true when information about cohort abundance is noisy or lacking.

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