LemnaTec provides sensor-based phenotyping technology and software to facilitate plant science and breeding. Their systems use multiple sensors and imaging to capture quantitative data on plant phenotypes, including size, morphology, water status, fluorescence, and hyperspectral indices. This comprehensive digital phenotyping data is analyzed using LemnaTec's software to generate metrics and identify traits. Their systems range from laboratory to greenhouse to field use, automating data collection for high-throughput screening. The quantitative data supports research in plant health, breeding, and understanding plant responses and genetics.
Brief informations on technologies available for high throughput field based phenomics for plant breeding experiments. The instrumentations and technologies presented here are based on the year 2015. Phenomics is expanding area of plant science as more technogies and latest instruments were introduced to the scientific community
High throughput phenotyping are fully automated facilities in greenhouses or growth chambers with robotics, precise environmental control, and remote sensing techniques to assess plant growth and performance
Brief informations on technologies available for high throughput field based phenomics for plant breeding experiments. The instrumentations and technologies presented here are based on the year 2015. Phenomics is expanding area of plant science as more technogies and latest instruments were introduced to the scientific community
High throughput phenotyping are fully automated facilities in greenhouses or growth chambers with robotics, precise environmental control, and remote sensing techniques to assess plant growth and performance
Affordable field high-throughput phenotyping - some tipsCIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Seminar presentation entitled 'Towards the development of cost-effective and moderate throughput plant phenotyping system' that was formerly presented during Regional Training Course on Mutation Breeding and Efficiency Enhancing Techniques held by International Atomic Energy Agency (IAEA) 10-20 VI 2014 (Seibersdorf, Austria). Enjoy & share comments!
Perspectives and Challenges of Phenotyping in Crop Improvement. - Copy.pptxRonikaThakur
Plant breeding programmes have been supplemented with the rapid advancements in modern technology. But these cannot be exploited fully until a précised phenotypic data is available which can bridge the gap between Genotype and Environment.
So, this presentation is made to have an overview how the advanced high throughput phenotyping platforms are playing a crucial role in the crop improvement.
High Throughput Plant Phenotyping in Crop ImprovementKhushbu
Plant phenomics is a high-throughput path-breaking area that meets all the requirements for the collection of accurate, rapid and multi-faceted phenotypic data. Traditional phenotyping tools are generally low-throughput, labor-intensive, which limits high efficiency and are prone to human error (Atefi et al. 2021). High throughput phenomics (HTP) technologies are essential to avoid human error and to reduce time consumption while phenotyping large germplasm populations (Pasala and Pandey, 2020). HTP is an emerging area with numerous applications that combines plant biology, sensing technology and robotics aiding crop improvement programs. Plant phenomics is the study of plant growth, performance and composition. (Atefi et al. 2021)
Forward phenomics uses phenotyping tools to discriminate the useful germplasm having desirable traits among a collection of germplasm. This leads to identification of the ‘best of the best’ germplasm. Thus in reverse phenomics, we discover mechanisms which make ‘best’ varieties the best (Jitender et al. 2015).
High Throughput Plant Phenotyping under three scenarios: greenhouses and growth chambers under strictly controlled conditions; ground-based proximal phenotyping in the field and aerial based platforms (Araus et al 2018). Root system architecture (RSA) phenotyping in situ is challenging, RADIX (a rhizoslide platform used to screen the shoots and roots).
Application of plant phenotyping methods as a part of breeding programs has developed into an important research tool that facilitates breeders to develop cultivars with higher adaptability under different environmental conditions. Remote sensing with Unmanned Aerial Vehicles (UAVs ) has emerged as highly efficient and accurate used to determine crop performance and biomass estimation. Current advanced techniques include thermal, near-infrared sensing, fluorescence imaging, 3D scanning, RGB imaging, multispectral and hyperspectral sensing are lucratively used for plant growth and development identifcation, quantification and monitoring; disease monitoring and abiotic stress tolerance. The integration of crop functional structure with remote sensing, geography information systems, GPS technologies, cloud computing, decision support systems will promote the development of digital agriculture and provide technical support for modern agriculture (Song et al. 2021). The robust and user-friendly post-processing and analysis tools for processing and interpreting raw data are urgently needed and should be improved (Yang et al. 2020).
Guidelines for the Conduct of Tests for DUS On Chilli (Hot Pepper), Bell (Sw...kartoori sai santhosh
Guidelines for the Conduct of Tests for Distinctiveness, Uniformity and Stability On
Chilli (Hot Pepper), Bell (Sweet) Pepper and Paprika(Capsicum annuum L.)
GRover: developing sensors for vineyard use Amanda Woods
GRover: developing sensors for vineyard use by Everard Edwards, Matt Siebers, Mark Thomas & Rob Walker, CSIRO Australia. Presented at the Precision Viticulture of the Riverland event on 1st Dec 2016. This presentation includes information on sensors for the vineyard.
Affordable field high-throughput phenotyping - some tipsCIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Seminar presentation entitled 'Towards the development of cost-effective and moderate throughput plant phenotyping system' that was formerly presented during Regional Training Course on Mutation Breeding and Efficiency Enhancing Techniques held by International Atomic Energy Agency (IAEA) 10-20 VI 2014 (Seibersdorf, Austria). Enjoy & share comments!
Perspectives and Challenges of Phenotyping in Crop Improvement. - Copy.pptxRonikaThakur
Plant breeding programmes have been supplemented with the rapid advancements in modern technology. But these cannot be exploited fully until a précised phenotypic data is available which can bridge the gap between Genotype and Environment.
So, this presentation is made to have an overview how the advanced high throughput phenotyping platforms are playing a crucial role in the crop improvement.
High Throughput Plant Phenotyping in Crop ImprovementKhushbu
Plant phenomics is a high-throughput path-breaking area that meets all the requirements for the collection of accurate, rapid and multi-faceted phenotypic data. Traditional phenotyping tools are generally low-throughput, labor-intensive, which limits high efficiency and are prone to human error (Atefi et al. 2021). High throughput phenomics (HTP) technologies are essential to avoid human error and to reduce time consumption while phenotyping large germplasm populations (Pasala and Pandey, 2020). HTP is an emerging area with numerous applications that combines plant biology, sensing technology and robotics aiding crop improvement programs. Plant phenomics is the study of plant growth, performance and composition. (Atefi et al. 2021)
Forward phenomics uses phenotyping tools to discriminate the useful germplasm having desirable traits among a collection of germplasm. This leads to identification of the ‘best of the best’ germplasm. Thus in reverse phenomics, we discover mechanisms which make ‘best’ varieties the best (Jitender et al. 2015).
High Throughput Plant Phenotyping under three scenarios: greenhouses and growth chambers under strictly controlled conditions; ground-based proximal phenotyping in the field and aerial based platforms (Araus et al 2018). Root system architecture (RSA) phenotyping in situ is challenging, RADIX (a rhizoslide platform used to screen the shoots and roots).
Application of plant phenotyping methods as a part of breeding programs has developed into an important research tool that facilitates breeders to develop cultivars with higher adaptability under different environmental conditions. Remote sensing with Unmanned Aerial Vehicles (UAVs ) has emerged as highly efficient and accurate used to determine crop performance and biomass estimation. Current advanced techniques include thermal, near-infrared sensing, fluorescence imaging, 3D scanning, RGB imaging, multispectral and hyperspectral sensing are lucratively used for plant growth and development identifcation, quantification and monitoring; disease monitoring and abiotic stress tolerance. The integration of crop functional structure with remote sensing, geography information systems, GPS technologies, cloud computing, decision support systems will promote the development of digital agriculture and provide technical support for modern agriculture (Song et al. 2021). The robust and user-friendly post-processing and analysis tools for processing and interpreting raw data are urgently needed and should be improved (Yang et al. 2020).
Guidelines for the Conduct of Tests for DUS On Chilli (Hot Pepper), Bell (Sw...kartoori sai santhosh
Guidelines for the Conduct of Tests for Distinctiveness, Uniformity and Stability On
Chilli (Hot Pepper), Bell (Sweet) Pepper and Paprika(Capsicum annuum L.)
GRover: developing sensors for vineyard use Amanda Woods
GRover: developing sensors for vineyard use by Everard Edwards, Matt Siebers, Mark Thomas & Rob Walker, CSIRO Australia. Presented at the Precision Viticulture of the Riverland event on 1st Dec 2016. This presentation includes information on sensors for the vineyard.
Measuring and mapping canopy traits from the lab to the field: sun-induced fl...CIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Automatic Image Processing for Agriculture through specific ENVI Modules (add...Esri
Presentation by L. García Torres, J.J. Caballero-Novella, D. Gómez-Candón and F. López-Granados from Institue for Sustainable Agriculture (CSIC) on Esri European User Conference 2011.
New remote and proximal sensing methodologies in high throughput field phenot...CIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Introduction to digital image processing, image processing, digital image, analog image, formation of digital image, level of digital image processing, components of a digital image processing system, advantages of digital image processing, limitations of digital image processing, fields of digital image processing, ultrasound imaging, x-ray imaging, SEM, PET, TEM
Use of sensor technology for real monitoring quality of vegetable cropsBaban Jeet
Use of sensor technology for real monitoring of the quality of vegetable crops; Recent advances in post-harvest to reduce food waste in vegetable crops
Thermo Fisher Scientific India Pvt. Ltd. (NYSE: TMO) is the world leader in serving science, with revenues of $18 billion and approximately 55,000 employees globally. Our mission through our premier brands Thermo Scientific, Applied Bio Systems, Invitrogen, Fisher Scientific and Unity Lab Services, we offer an unmatched combination of innovative is to enable our customers to make the world healthier, cleaner and safer. We help our customers accelerate life sciences research, solve complex analytical challenges, improve patient diagnostics and increase laboratory productivity. We offer an unmatched combination of innovative technologies, purchasing convenience and comprehensive support.
Andy J Value Of A Coordinate Montpellier Nov 2009CIAT
Presentation at TDWG 2009 in montpellier on the value of geographic coordinates for exploring agricultural biodiversity patterns, and influencing conservation policy.
Presentation made by Andy Jarvis from the Decision and Policy Analysis Program of the International Centre for Tropical Agriculture (CIAT). Delivered at the TDWG conference 2009 in Montpellier, France in November.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
3. Quantitative or qualitative data?
Phenome
Metabolome
Proteome
Transcriptome
Genome
Still many studies give only
qualitative data for phenotypic
properties, while underlying
biochemistry and molecular
biology of course is given in a
quantitative manner.
5. Do we need numerical phenotypic data?
Regardless how you modify the
crop, you are interested in the
resulting phenotypes, and you
need to quantify the success of
modification!
7. Componets of phenotyping
Plants (biological objects) Environment
Experimental setup
Sensors, measurement
platforms, software
Phenotyping
result
8. LemnaTec OS – advanced phenotyping software
LemnaControl
Control of hardware plant carriers, sensors, watering, spraying
API for integration of a wide range of sensors
Programmable interface for non-LemnaTec equipment
Centralised data acquisition
LemnaGrid
Graphical programming of image analysis
Library of image processing algorithms including hyperspectral data
API for integration of third party image processing software
LemnaBase
Database management
Open access to databases with full documentation
Graphical display of images, data and analysis
Metadata
Lemna-R
Easy access to all data with plotting function
Integrates seamlessly with R statistics
Configurable data processing functions
Visualisation of data and images
9. Drag & drop data processing
Plants surface and shape determination with LemnaGrid software
10. Digital phenotyping – multiple sensors deliver comprehensive data sets
SENSOR MEASURED PARAMETERS DERIVED BIOLOGICAL INFORMATION
VIS camera
Dimensions ("digital biomass"),
geometry, colour
Growth, biomass, development, stress
Laser scanner 3D point cloud Growth, geometry, organ-resolved information
Hyperspectral camera Spectrally resolved images
Biomass, physiology, pigments, water status,
stress, diseases, vegetation indices
PSII camera Chlorophyll fluorescence Photosynthetic parameters
IR camera Surface heat emission Temperatures, transpiration
NIR camera Reflectance due to water content Water status
Fluo-camera Fluorescence signals
Chlorophyll, senescence, fluorescent pigments,
biomarkers
11. Mahlein, Anne-Katrin (2016): Plant Disease Detection by
Imaging Sensors – Parallels and Specific Demands for Precision
Agriculture and Plant Phenotyping. In: Plant Disease 100 (2), S.
241–251. DOI: 10.1094/PDIS-03-15-0340-FE.
Multi-level phenotyping – example plant diseases
12. Laboratory Systems
PhenoBox
Entry level bench-top instrument
Small footprint, low cost
Lab Scanalyzer
Advanced bench-top instrument
Wide range of sensors
Top and side view
HTS Lab Scanalyzer
Reproducible screening
High throughput
Automation options
High precision positioning
Common applications
Seedlings
In-vitro germination tests
Population screens
Gene functions
Herbicide, insecticide tests
Ecotoxicology – duckweed test
Feeding and motility tests with insects, mites etc.
Microbial colony counting
13. Feeding tests
HT-screening for leaf eating organisms
feeding assays
resistance screens
organism sizes
mortality assessment
Saran, Raj K.; Ziegler, Melissa; Kudlie, Sara; Harrison, Danielle; Leva, David M.;
Scherer, Clay; Coffelt, Mark A. (2014): Behavioral Effects and Tunneling
Responses of Eastern Subterranean Termites (Isoptera: Rhinotermitidae)
Exposed to Chlorantraniliprole-Treated Soils. In: Journal of Economic Entomology
107 (5), S. 1878–1889. DOI: 10.1603/EC11393.
15. Laser Scanner – 3D point cloud
Dornbusch, T. et al. (2014) Differentially Phased Leaf Growth and Movements in Arabidopsis Depend on Coordinated Circadian and Light
Regulation. The Plant Cell 26, 3911–39212
Dornbusch, T. et al. (2012) Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis - a novel phenotyping approach using laser
scanning. Functional Plant Biology 39, 860
16. Greenhouse Scanalyzer System
Automated indoor phenotyping
Complete solutions
Modular construction
Fully configurable
Robust and reliable
Features
Multiple imaging for 3D calculations
Weighing and watering
Plant density optimization
Plant tracking
Multiple Sensors
18. Plant and soil water status
Near infrared light (NIR) reflectance relates to tissue water content
measuring water distribution within plants or soil and dynamic changes in time
wheat dried down over 16 h at elevated
temperature
0h
8h
4h
16h
0h 2h 4h 6h 8h
Soil water content
monitoring
19. Fluorescence imaging
Fluorescence signals
Senescence, autofluorescence
Hairmansis A, Berger B, Tester M, Roy SJ (2014) Image-based phenotyping for non-destructive
screening of different salinity tolerance traits in rice. Rice 7: 16
21. Cereals – response to water limitation
Measurement – parameters – information – knowledge
Images – plant area data – biomass calculation – QTL discovery
22. Field Scanalyzer System
Automated outdoor phenotyping
Modular construction
Fully configurable
Comprehensive datasets
Repeatable measurements
High precision positioning
Fully automated 24 x 7
Robust and weatherproof
Multiple sensors
24. Enviormental Sensors – „ecotyping“
• CO2 Sensor
• NDVI Sensor
• Active Reflectance Sensor (Crop Circle)
• PAR Sensor
• Color Sensor
• General Enviormental Sensors
• Rain
• Wind
• Light
25. Phenotyping sensors
• 2x 9MP RGB Camera
• Mounted on a flexible base plate
• Cooled Housing
• FLIR thermal camera
• PSII camera (Kautsky effect)
• Laser scanners - special development by Fraunhofer IIS
• 0.6m Scan width
• 1.5m Scan depth (adjustable)
• 0.25mm point to point distance
• 2x Side looking with different setup
• Hyperspectral cameras
28. PSII imaging – chlorophyll fluorescence parameters
F0 – dark adapted
Fm – dark adapted
Maximum quantum efficiency of PSII: (Fm - F0) / Fm
(Fm - F0) / Fm
1
0
30. Hyperspectral data and vegetation indices
Modified Chlorophyll absorption in Reflectance index (MCARI)
Modified Chlorophyll Absorption in Reflectance Index (MCARI1)
Soil adjusted vegetation indices (XSAVI)
Optimised Soil Adjusted Vegetation Index (OSAVI)
Gitelson and Merzlyak Indiex1
Gitelson and Merzlyak Indicex2
Red Edge Normalized Difference Vegetation Index NDVI705
Modified Red Edge Simple Ratio Index
Modified Red Edge Normalized Difference Vegetation Index
Greenness Index
Vogelmann Indicex1
Vogelmann Index2
Vogelmann Index3
Transformed CAR Index (TCARI)
Simple Ratio Pigment Index (SRPI)
Normalised Phaeophytinization Index NPQI
Carotenoid Reflectance Index 1
Carotenoid Reflectance Index 2
Anthocyanin Reflectance Index 1
Anthocyanin Reflectance Index 2
Plant Senescence Reflectance Index
Photochemical Reflectance Index (PRI)
Nitrogen related index NRI1510
Nitrogen related index NRI850
Normalized Difference Nitrogen Index
Normalized Pigment Chlorophyll Index (NPCI)
Carter Index1
Carter Index2
Lichtenthaler Index1
Lichtenthaler Index2
Structure Insensitive Pigment Index (SIPI)
NVDI Turf Colorimeter
Water Band Index
Water index (Thiel, Rath , Ruckelshausen)
Normalized Difference Water Index
Moisture Stress Index
Normalized Difference Infrared Index
Desease-Water Stress Index 1
Desease-Water Stress Index 2
Desease-Water Stress Index 3
Desease-Water Stress Index 4
Desease-Water Stress Index 5
Leaf structure index R1110/R810
Normalized Difference Lignin Index
Cellulose Absorption Index
extended VNIR
VNIR
normal NIR
SWIR
250 500 750 1000 1250 1500 1750 2000 2250 2500
40. Pre-conference satellite
event, 24th and 25th
October
HOTEL NHOW BERLIN
Wednesday | Oct 26th 2016 | 9:00
until Thursday | Oct 27th 2016 | 18:00
IPPN Symposium at
CIMMYT
El Batan, Mexico
13.-15.12.2016
Phenotyping conferences 2016