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The TERRA-REF project is phenotyping the same genotypes of sorghum at multiple locations
Automated Lemnatec Scanalyzer Field System at Maricopa Agricultural Center (MAC)
PhenoTractors on parallel plots at MAC and Kansas State University (KSU)
UAV platform on parallel plots at KSU
Controlled-environment phenotyping systems at the Danforth Center
Manually collected field data at all locations
Whole genome resequencing is being carried out on ~400 sorghum accessions to understand the landscape of genetic variation in the selected germplasm and enable high-resolution mapping of bioenergy traits with genome wide association studies (GWAS). Additionally, ~200 sorghum recombinant inbred lines (RILs) will be characterized with ~400,000 genetic markers using genotyping-by-sequencing (Morris et al., 2013) for trait dissection in the RIL population and testcross hybrids of the RIL population.
Three hundred thirty one lines were planted in 2016. Plantings occurred both under and west of the gantry system.
Field layouts under the gantry and west of the gantry in 2016.
The Lemnatec Scanalyzer Field Scanner System is the largest field crop analytics robot in the world. This high-throughput phenotyping field-scanning robot has a 30-ton steel gantry that autonomously moves along two 200-meter steel rails while continuously imaging the crops growing below it with a diverse array of cameras and sensors.
Twelve sensors are attached to the system. Detailed information for each sensor including name, variable measured, and field of view are available here. The planned sensor missions and their objectives for 2016 are available here.
The PhenoTractor at MAC is fitted with a sensor frame that supports a real time kinematic (RTK) satellite navigation antenna, a sonar transducer, an infrared temperature (IRT) scanner, and three GreenSeeker crop sensing systems.
Coming 2017
In progress
The Scanalyzer 3D platform at the Bellwether Foundation Phenotyping Facility at the Donald Danforth Plant Science Center consists of multiple digital imaging chambers connected to the Conviron growth house by a conveyor belt system, resulting in a continuous imaging loop. Plants are imaged from the top and/or multiple sides, followed by digital construction of images for analysis.
RGB imaging allows visualization and quantification of plant color and structural morphology, such as leaf area, stem diameter and plant height.
NIR imaging enables visualization of water distribution in plants in the near infrared spectrum of 900–1700 nm.
Fluorescent imaging uses red light excitation to visualize chlorophyll fluorescence between 680 – 900 nm. The system is equipped with a dark adaptation tunnel preceding the fluorescent imaging chamber, allowing the analysis of photosystem II efficiency.
The LemnaTec software suite is used to program and control the Scanalyzer platform, analyze the digital images and mine resulting data. Data and images are saved and stored on a secure server for further review or reanalysis.
PhenoTractor - Coming 2017
UAV - Coming 2017
Manually collected field data - Coming 2017
Coming 2017
This section describes sensor calibration processes and how to access additional information about specific calibration protocols, calibration targets, and associated reference data.
Calibration protocols have been defined by LemnaTec in cooperation with vendors and the TERRA-REF Sensor Steering Committee. Draft calibration protocols are currently in Google Drive and have been incorporated into the LemnaTec Scanalyzer Field sensor documentation.
A detailed calibration process is also provided for the Hyperspectral sensors, with further information below.
The following calibration targets are available:
Aluminum 3D test object
The environmental sensor has been calibrated by LemnaTec. The output of the spectrometer is raw counts, users will need to use the calibration files to convert to units of µW m-2 s-1, taking into account the bandwidth of the chip (0.4nm) if converting to µmol m-2 s-1.
Calibration reference data is available via Globus /sites/ua-mac/EnvironmentLogger/CalibrationDataor in Github Calibrations.zip
Sources:
For the SWIR and VNIR sensors, factory calibration is repeated each year using the calibration lamp provided by Headwall. To convert the hyperspectral exposure image to reflectance requires the wavelength-dependent, factory calibrated reflectance of the spectralon at all VNIR and SWIR wavelengths and a good image of a spectralon panel from each camera. This includes periodic measurements of a white spectralon reflectance panel run with 20ms exposure to match panel calibration.
Dark reference measurement:
VNIR
Dark measurement for VNIR camera is taken at exposure times 20, 25, 30, 35, 40, 45, 50, 55ms.
Data is in the same hypercube format with 180-200 lines, 955 bands, and 1600 pixel samples.
Data is available on Globus in /gantry_data/VNIR-DarkRef/ or via Google Drive.
Measurement was done using Headwall software, so there is no LemnaTec json file.
The name of the folder is the exposure time. "current setting exposure" is showing the exposure time in ms.
Custom workflow to process the calibration files.
SWIR;
Dark counts handled internally, so no calibration files are necessary.
White reference measurement:
VNIR
White measurement for VNIR camera is taken at exposure times 20, 25, 30, 25, 40, 25, 50, 55 ms.
The name of the folder is the exposure time. Data are 1600 sample, 955 bands and 268-298 lines. White reference is located in the lines between 60 to 100 and in the samples between 600 to 1000.
Data is available via Google Drive.
The white reference scans was done at around 1pm ( one hour after solar noon). I don’t see the saturation with 20ms and 25ms exposure time.
For the calibration, this needs to be subtracted from the dark current in the same sample, band and exposure time.
In the following file, I stored an extra file named "CorrectedWhite_raw". This file includes only a single white pixel( one line, one sample) in 955 bands for each exposure time. Data is stored in the similar format but it doesnot include any extra files like frameIndex, image, header ,..
https:\/\/drive.google.com\/file\/d\/0ByXIACImwxA7dVNHa3pTYkFjdWc\/view?usp=sharing
Let me know if you have issue with opening the files.
LemnaTec applied calibration matrix to the 3D scanners.
Source: https://github.com/terraref/computing-pipeline/issues/185
There are calibrated reference panels and blackbody images taken with UAV sensors before and\/or after the each flight mission.
There are also 4 white,grey and black panels laid on the ground during the flight. Knowing the proprieties of these targets would helps us radiometrically correct the UAV images.
What are the reflectance properties of calibrated reference panels for multispectral camera?
What are the thermal properties of reference target for thermal camera?
What are the reflectance properties of the reference panels laid on the ground during the flight?
Is there any other ground truth data collected during the flight for aerial data processing, such as surface reflectance, temperature and other environmental data? These type of data would be helpful for further atmospheric correction.
There are two sets of reference reflectance panels: one that PDS uses, it is small, PDS will need to provide the specs; the second set consists of 4 8m x 8m canvas tarps, nominally 4%, 8%, 48% and 64% reflectance across vnir bands.
We have data from an ASD spectrometer on many but not all flight days that can be used to give the most accurate actual reflectances for each. Kelly Thorp can provide the numbers. The tarps are old and the dark targets are more reflective than nominal and light targets darker than nominal.
The thermal target is a passive black body, I dont know the surface emissivity, it is around 0.97. There are thermistors in the back of the metal plate to provide physical temperature of the body. The black body is stored in a wood box, insulated, to dampen thermal variations. Id guess it is accurate to 2C.
There is a met station on farm for air temperature, humidity, wind speed, wind direction, solar radiation. we have a sun photometer that can be used for atmospheric water vapor content but currently dont deploy it routinely.
No per-wavelength analysis of light produced by the halogen lights is available from the vendor for Showtec 240V\/75W. Measurements are available for a similar halogen bulb Philips Twistline Halogen 230V 50W 18072 in Github: MeasurementPhilipsHalogenSpot.xlsx.
Relative spectral response data is available for the following sensors:
NDVI
PRI
PAR
Where available, per device calibration certificates are included in the Device and Sensor information collections.
barcode scanning protractor
barcode scanning ruler
ceptometer (Decagon AccuPAR LP-80)
digital caliper
drying oven
forage chopper
hand shears
infrared thermometer
juice extractor
leaf area meter (Li-Cor 3100, Li-Cor Inc.)
leaf porometer (SC-1 Leaf Porometer, Decagon Devices)
leaf punch
meter stick
paper bags
portable photosynthesis system (Li-Cor 6400, Li-Cor Inc.)
scale
SPAD Meter (SPAD 502 Plus Chlorophyll Meter, Minolta)
spray paint
Pérez-Harguindeguy N., Díaz S., Garnier E., Lavorel S., Poorter H., Jaureguiberry P., Bret-Harte M. S., CornwellW. K., Craine J. M., Gurvich D. E., Urcelay C., Veneklaas E. J., Reich P. B., Poorter L., Wright I. J., Ray P., Enrico L.,Pausas J. G., de Vos A. C., Buchmann N., Funes G., Quétier F., Hodgson J. G., Thompson K., Morgan H. D., ter Steege H., van der Heijden M. G. A., Sack L., Blonder B., Poschlod P., Vaieretti M. V., Conti G., Staver A. C.,Aquino S., Cornelissen J. H. C. (2013) New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany 61 , 167–234. http://dx.doi.org/10.1071/BT12225
Vanderlip RL. 1993. How a sorghum plant develops. Manhattan, KS, USA: Kansas State University Cooperative Extension. Field Experiments in Crop Physiology. 2013, Jan 13. In PrometheusWiki. Retrieved 15:03,June 21, 2016, from http://www.publish.csiro.au/prometheuswiki/tiki-pagehistory.php?page=Field Experiments in Crop Physiology&preview=41
Photosynthesis / leaf chemistry from hyperspectral data references:
Shawn Serbin et al - Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature 2011 J Exp Bot
Additional Draft Protocols are available at https://docs.google.com/document/d/1iP8b97kmOyPmETQI_aWbgV_1V6QiKYLblq1jIqXLJ84/edit#
The following protocols have been contributed by TERRA-REF team members:
Field Scanner - Coming 2017
Genomics - Coming 2017
A template for documenting protocols .
SenseFly eBee fixed-wing drone
Hexacopter
UAV data are collected using one of three cameras:
5-band
4-band + RGB
SenseFly thermal
Cameras are carried singly or in tandem on the SenseFly eBee fixed-wing drone (Sequoia and thermoMap, individually only), or a hexacopter (RedEdge or Sequoia, individually or in tandem).
No radiometric calibration was conducted as of Nov 5, 2016.
QGIS software was used to confirm geospatial alignment of NDVI geotiffs with shape files containing geolocated positions of the rail foundations. A shape file containing polygons aligning with the middle two rows of each of the 350 experimental units (for sorghum crop Aug-Nov 2016) was kindly generated by Dr. A French of USDA-ARS. Zonal Statistics in QGIS was used to calculate NDVI means for each plot polygon.
Authors: Matthew Maimaitiyiming, Wasit Wulamu, and David LeBauer
Center for Sustainability, Saint Louis University, St. Louis, MO 63108
This document provides a brief summary of methods, procedures, and workflows to process the tractor data.
Content modified from .
Tractor
Sensors
Sonar Transducer
GreenSeeker Multispectral Radiometer
Infrared Thermal Sensor
The Tractor-based plant phenotyping system (Phenotractor) was built on a LeeAgra 3434 DL open rider sprayer. The vehicle has a clearance of 1.93 m. A boom attached to the front end of the tractor frame holds the sensors, data loggers, and other instrumentation components including enclosure boxes and cables. The boom can be moved up and down with sensors remaining on a horizonal plane. An isolated secondary power source supplies 12-V direct current to the electronic components used for phenotyping.
The phenotractor was equipped with three types of sensors for measuring plant height, temperature and canopy spectral reflectance. A RTK GPS was installed on top of the tractor, see the figure below.
The distance from canopy to sensor position was measured with a sonar proximity sensor ($S\rm{output}$, in mm). Canopy height ($CH$) was determined by combining sonar and GPS elevation data (expressed as meter above sea level). An elevation survey was conducted to determine a baseline reference elevation ($E\rm{ref}$) for the gantry field. CH was computed according to the following equation:
where $E_rm{s}$ is sensor elevation, which was calculated by subtracting the vertical offset between the GPS antenna and sonar sensor from GPS antenna elevation.
Canopy spectral reflectance was measured with GreenSeeker sensors and the reflectance data were used to calculate NDVI (Normalized Difference Vegetation Index). GreenSeeker sensors record reflected light energy in near infrared (780 ± 15 nm) and red (660 ± 10 nm ) portion electromagnetic spectrum from top of the canopy by using a self-illuminated light source. NDVI was calculated using following equation:
Where $\rho\rm{NIR}$ and $\rho\rm{red}$ and ρ_red represent fraction of reflected energy in near infrared and red spectral regions, respectively.
Georefencing was carried out using a specially developed Quantum GIS (GGIS, www.qgis.org ) plug-in by Andrade-Sanchez et al. (2014) during post processing. Latitude and longitude coordinates were converted to UTM coordinate system. Offset from the sensors to the GPS position on the tractor heading were computed and corrected. Next, the tractor data, which uses UTM Zone 12 (MAC coordinates), was transformed to EPSG:4326 (WGS84) USDA coordinates by performing a linear shifting as follows:
Latitude: $U_y = M_y – 0.000015258894$
Longitude: $U_x = M_x + 0.000020308287$
Automated VIS and NIR imaging in a controlled growth environment
ProMix BRK20 + 14-14-14 Osmocote pots; pre-filled by Hummert Sorghum seed
Conviron Growth House
LemnaTec moving field conveyor belt system
Scanalyzer 3D platform
Planting
Plant directly into phenotyping pots
Chamber Conditions
Pre-growth (11 days) and Phenotying (11 days)
14 hour photoperiod
32oC day/22oC night temperature
60% relative humidity
700 umol/m2/s light
Watering Conditions
Prior to phenotyping, plants watered daily
The first night after loading, plants watered 1× by treatment group to 100% field capacity (fc)
Days 2 – 12, plants watered 2× daily by treatment group (100% or 30% FC) to target weight
Automation
Left shift lane rotation within each GH, during overnight watering jobs
VIS (TV and 2 x SV), NIR (TV and 2 x SV) imaging daily
Field capacity = 200% GWC (200 g water/100 g soil), based upon extensive GWC testing done by Skyler Mitchell
Target weight (fc) = [(water weight at % fc) + [(average weight of carrier/saucer) + (dry soil weight) + (pot weight)]
Water weight at 100% fc = dry soil weight * (%GWC/100)
Water weight at 30% fc = water weight at 100% fc * 0.30
Standard flight altitude is 44m with 75% image overlap (both sequentially and laterally), and missions are programmed and managed by either or senseFly .
Pix4D software was used to generate gray-scale orthomosaic geotiff files containing NDVI data after georegistration to the WGS84/UTM 12 N coordinate reference system using three to five 2D geo-located ground control points. These are manually matched to 5-40 images each. Ground control points for the Lemnatec Field Scanner are on the concrete pylons and were geolocated using an RTK base station maintained by the USDA-ARS at Maricopa (see section on ).
MicaSense:
SenseFly
QGIS
An Infrared radiometer (IRT) sensors were used measure canopy temperature and temperature values were recoded as degree Celsius (°C).
where $U_y$ and $U_x$ are latitude and longitude in USDA coordinate system, and $M_y$ and $M_x$ are latitude and longitude in MAC coordinate system (see ). Finally, georeferenced tractor data was overlaid on the gantry field polygon and mean value for each plot/genotype was calculated using the data points that fall inside the plot polygon within ArcGIS Version 10.2 (ESRI. Redlands, CA).
Andrade-Sanchez, Pedro, Michael A. Gore, John T. Heun, Kelly R. Thorp, A. Elizabete Carmo-Silva, Andrew N. French, Michael E. Salvucci, and Jeffrey W. White. "Development and evaluation of a field-based high-throughput phenotyping platform." Functional Plant Biology 41, no. 1 (2014): 68-79.
Variable
Canopy Height
Canopy height for single row of central 2 data rows of 4-row plot. Measured in cm using meter stick, taken at the height representing the plot 'potential', ignoring stunted plants. The canopy height was measured as the height of the foliage (not the inflorescence) at the general top of the canopy where the upper leaves bend and/or establish a canopy surface that would support a very light horizontal object (imagining a light sheet of rigid plastic foam), discounting rare or exceptional leaves in the upper-most 2 or 3 percentile.
Panicle Height
Height of the top of the inflorescence panicle for single central data row of 4-row plot, when panicle extends notably above canopy height.
Seedling Vigor and Emergence
Count the number of emerging seedlings at about 20% emergence, and then repeat every other day until final stand is achieved. A seedling is defined as emerged when the coleoptile is visible above the soil surface. Final stand is defined as when a similar count +/- 5% is achieved on successive counts 1-2 days apart. Count seedlings in the entire plot. Two Alternatives 1. Explicitly count number of plants emerged 2. For each plot, assess % germination in categories (e.g. [0,20], [20,40], …) This is the standard method
Canopy closure and leaf area index
Sunfleck ceptometer readings will be taken at least monthly to determine radiation interception and canopy closure. Using e.g. Decagon AccuPAR LP-80. Leaf area index will be calculated using Beer's Law for light extinction. A total of 5 readings will be taken per plot and averaged. Readings will be taken on clear days. Incident light will be measured at least once per rep. NDVI will also be measured weekly using a tractor mounted unit until the tractor can no longer navigate through the field due to the height of the crop. References:Prometheus Wiki http://prometheuswiki.publish.csiro.au/tiki-index.php?page=Canopy+light+interception+assessment+-+from+DC20
Leaf Architecture / Leaf erectness
Barcode scanning protractor is used to measure youngest fully emerged leaf
Leaf Width
Barcode scanning ruler measured at the widest part of the leaf
Stem number
Manually count the total number of stems in the plot will be counted bi-weekly after thinning for all plants in the plot.
Stem diameter
Stem diameter for each of 10 plants per plot will be measured with a digital caliper at 10 and 150 cm every month. For each plant take a few diameter samples and record the most common value. Use a black sharpie to mark the location at which the sample was taken.
Canopy Height
An "eyeball" estimate of plant height for the entire plot will be taken weekly beginning at the 5-leaf stage. Canopy height, view the canopy horizontally with a measuring stick, taking the height where a light piece of foam would rest on the canopy. Estimate the median height of healthy standing plots, ignoring plants that look really bad (e.g. are lodged). For method development: on subset of plots (10), capture the distribution of heights, e.g. max, min, median, upper and lower quantiles.
Lodging
There are three measures: 1. Percent lodging 0-100 scale 2. Lodging severity 0-100 scale 3. Lodging score 0-100 scale 4. Whether this is stalk or root lodging (categorical 'root', 'stalk') A lodging score will be taken weekly once lodging is observed. The lodging score will be recorded as a percentage and is a combination of the fraction of the plants lodged and the severity of lodging. For example, if 50% of the plants are 50% lodged, then the lodging score would be 25%. The severity of lodging is determined by how far the plants are leaning from vertical. If a plant is laying on the ground the severity of lodging is 100%. If a plant is leaning 45 degrees from vertical, then the severity of lodging is 50%. How to differentiate between stalk lodging and root lodging: scoring 'lodging' implies diagnosing a cause of inclined stems. A better approach may be a visual estimate of a range, with an optional note for root or shoot lodging. Done as deflection from vertical, this might look like:Min_angle Max_angle Loding_type0 1010 4530 60 R20 40 S…Where R = root lodging, S = stem lodging. Since stems are usually curved, the question remains of what reference height to consider?
Above-ground yield
Alleyways will be trimmed by hand with a weed whacker with a blade to accommodate space required between plots for a 2-row forage chopper. Actual plot length will be measured from the first to last stalk cut by the forage chopper. The stalks trimmed by hand will be spray painted to delineate them from stalks in the harvest area. The chopped forage will be weighed in a bag and a 2-quart sample removed for moisture and quality analysis. The sample will be dried in an oven at 65 C until constant weight is achieved. The dried forage will be ground and submitted for quality analysis. Sorghum Checkoff provided 1.5 pg protocol
Total biomass and tissue partitioning
Plants will be (destructively?) sampled (from west of gantry plots?) five times during the season from the 5 leaf stage through final harvest. The area sampled will be 1 meter of row. The plants will be cut off at ground level and immediately placed in a cooled ice chest for transport from the field to the laboratory where they were stored at 5°C until processing.
Allometry
Plant height will be measured from the base of the plant to the point where the top leaf blade is perpendicular to the stem. The number of stems and their average phenological stage will be recorded. Leaves will be removed from the stem at the collar and separated into green and brown leaves.
Leaf Area Index (LAI)
Leaf area of green leaves will be measured with a leaf area meter (Li-Cor 3100, Li-Cor Inc., Lincoln, NE, USA). Heads will be separated from the stems. Stem area will be estimated from stem length (without the head ) x diameter. The stems, brown and green leaves, and heads will be dried separately in an oven at 65°C for 2–4 d and weighed. Leaf area index and stem area index will be calculated.
Specific Leaf Area (SLA)
Specific leaf area will be calculated by dividing green leaf area by green leaf weight.
Phenology
Phenology will be determined according to Vanderlip (1993). Before heading, developmental stages were based on the appearance of the leaf collars. After heading, phenological stages were determined based on the development of the grain. Numbers ranging from 1 (50% of plants heading) to 7 (50% of plants at physiological maturity) were assigned to designate growth stage after the vegetative period. Before heading, growth stages represent mean leaf number of all plants and not the most advanced 50% as was done after headingReference: https://www.bookstore.ksre.ksu.edu/pubs/S3.pdf
Days to flag leaf emergence
Days to spike emergence
Days to anthesis/flowering
Once anthesis begins, anthesis will be noted 3 times per week until anthesis ends. Anthesis is defined as when 50% of the plants have one or more anthers showing.
Maturity pattern
Once maturity begins, maturity will be noted 3 times per week until maturity ends. Maturity is defined as when 50% of the plants have reached black layer.
Moisture content
Forage moisture content will be determined at final harvest and from the biomass samples by weighing the forage before and after drying in an oven at 65 C for a minimum of 48 h. How large is the sample? ~ 1 pound in a lunchbag, 2 samples per plotHow will it be packaged / labeled?Subsamples?
Lignin content
Determined by NIRS from the moisture sample at final harvest.
BTU/DW
Determined by NIRS from the moisture sample at final harvest.
Juice extraction
Juice will be extracted from stalks from the biomass samples at final harvest using a sweet sorghum mill. The juice will be weighed and brix measured. Brix concentration in the juice – Brix will be measured in the juice extracted as described above.
Plant temperature
A hand-held infrared thermometer will be used to measure plant temperature bi-weekly. A total of 5 readings will be recorded per plot within 2 hours of solar noon.
Plant color
A Minolta SPAD meter will be used to record plant color on plants using the most recently fully expanded leaf on a bi-weekly basis.
Photosynthesis
Using LiCOR 6400, measure A-Ci and A-Q curves to estimate parameters of Collatz model of C4 photosynthesis coupled to the Ball Berry model of stomatal conductance. One reading from the youngest fully expanded leaf. These readings will be taken monthly within 2 hours of solar noon.
Transpiration/stomatal conductance
Stomatal conductance was assessed using a leaf porometer (Decagon Devices, Pullman, WA) by taking 5 readings per plot on most recently fully expanded leaves. Readings will be taken on the 12 photoperiod sensitive lines in the biomass association panel. These readings will be taken bi-weekly and within 2 hours of solar noon at least two times during the season.
ATLAS LEOTI PI_144134 PI_145619 PI_145626 PI_145632 PI_145633 PI_146890 PI_147224 PI_152591 PI_152651 PI_152694 PI_152727 PI_152728 PI_152730 PI_152733 PI_152751 PI_152771 PI_152816 PI_152828 PI_152860 PI_152862 PI_152923 PI_152961 PI_152963 PI_152965 PI_152966 PI_152967 PI_152971 PI_153877 PI_154750 PI_154844 PI_154846 PI_154944 PI_154987 PI_154988 PI_155149 PI_155516 PI_155760 PI_155885 PI_156178 PI_156203 PI_156217 PI_156268 PI_156326 PI_156330 PI_156393 PI_156463 PI_156487 PI_156871 PI_156890 PI_157030 PI_157033 PI_157035 PI_157804 PI_167093 PI_170787 PI_175919 PI_176766 PI_179749 PI_180348 PI_181080 PI_181083 PI_195754 PI_196049 PI_196583 PI_196586 PI_196598 PI_197542 PI_19770 PI_213900 PI_217691 PI_218112 PI_221548 PI_221651 PI_226096 PI_22913 PI_229841 PI_251672 PI_253986 PI_255239 PI_255744 PI_257599 PI_257600 PI_266927 PI_267573 PI_273465 PI_273969 PI_276837 PI_297130 PI_297155 PI_297171 PI_302252 PI_303658 PI_329256 PI_329286 PI_329299 PI_329300 PI_329301 PI_329310 PI_329319 PI_329326 PI_329333 PI_329338 PI_329351 PI_329394 PI_329403 PI_329435 PI_329440 PI_329465 PI_329466 PI_329471 PI_329473 PI_329478 PI_329480 PI_329501 PI_329506 PI_329510 PI_329511 PI_329517 PI_329518 PI_329519 PI_329541 PI_329545 PI_329546 PI_329550 PI_329569 PI_329570 PI_329584 PI_329585 PI_329605 PI_329614 PI_329615 PI_329618 PI_329632 PI_329644 PI_329645 PI_329646 PI_329665 PI_329673 PI_329699 PI_329702 PI_329710 PI_329711 PI_329841 PI_329843 PI_329864 PI_329865 PI_330168 PI_330169 PI_330181 PI_330182 PI_330184 PI_330185 PI_330195 PI_330196 PI_330199 PI_330796 PI_330803 PI_330807 PI_330833 PI_330858 PI_337680 PI_337689 PI_35038 PI_365512 PI_452542 PI_452619 PI_452692 PI_453696 PI_455217 PI_455221 PI_455280 PI_455301 PI_455307 PI_505717 PI_505722 PI_505735 PI_506030 PI_506069 PI_506114 PI_506122 PI_508366 PI_510757 PI_511355 PI_513898 PI_514456 PI_521019 PI_521152 PI_521280
PI_521290 PI_524475 PI_525049 PI_52606 PI_526905 PI_527045 PI_533792 PI_533902 PI_533998 PI_534120 PI_534165 PI_535783 PI_535785 PI_535792 PI_535793 PI_535794 PI_535795 PI_535796 PI_540518 PI_542718 PI_550604 PI_561840 PI_562730 PI_562732 PI_562781 PI_562897 PI_562970 PI_562971 PI_562981 PI_562982 PI_562985 PI_562990 PI_562991 PI_562994 PI_562997 PI_562998 PI_563002 PI_563006 PI_563009 PI_563020 PI_563021 PI_563022 PI_563032 PI_563196 PI_563222 PI_563295 PI_563329 PI_563330 PI_563331 PI_563332 PI_563338 PI_563348 PI_563350 PI_563355 PI_564163 PI_566819 PI_568717 PI_569090 PI_569097 PI_569148 PI_569244 PI_569264 PI_569416 PI_569418 PI_569419 PI_569420 PI_569421 PI_569422 PI_569423 PI_569425 PI_569427 PI_569433 PI_569435 PI_569443 PI_569444 PI_569445 PI_569447 PI_569452 PI_569453 PI_569454 PI_569455 PI_569457 PI_569458 PI_569459 PI_569460 PI_569462 PI_569465 PI_569886 PI_570031 PI_570038 PI_570042 PI_570047 PI_570053 PI_570071 PI_570073 PI_570074 PI_570075 PI_570076 PI_570085 PI_570087 PI_570090 PI_570091 PI_570096 PI_570106 PI_570109 PI_570110 PI_570114 PI_570145 PI_570254 PI_570371 PI_570373 PI_570388 PI_570393 PI_570400 PI_570431 PI_573193 PI_576399 PI_576401 PI_583832 PI_585406 PI_585448 PI_585452 PI_585454 PI_585461 PI_585467 PI_585577 PI_585608 PI_585954 PI_585961 PI_585966 PI_586435 PI_586443 PI_586541 PI_593916 PI_619807 PI_619838 PI_620072 PI_620157 PI_63715 PI_641807 PI_641810 PI_641815 PI_641817 PI_641821 PI_641824 PI_641829 PI_641830 PI_641835 PI_641836 PI_641850 PI_641860 PI_641862 PI_641892 PI_641909 PI_642998 PI_643008 PI_643016 PI_646242 PI_646251 PI_646266 PI_651491 PI_651493 PI_651495 PI_651496 PI_651497 PI_653616 PI_653617 PI_655972 PI_655978 PI_655981 PI_655983 PI_656015 PI_656026 PI_656035 PI_656065 PI_92270 PI_329471 PI_329506 PI_329569 PI_337680 PI_452692 PI_455217 PI_152730 PI_329311 NTJ2 M81e CK60B B_Az9504 ICSV700 China 17
Genomic data includes whole-genome resequencing data from the HudsonAlpha Institute for Biotechnology, Alabama for 384 samples for accessions from the sorghum Bioenergy Association Panel (BAP) and genotyping-by-sequencing (GBS) data from Kansas State University for 768 samples from a population of sorghum recombinant inbred lines (RIL).
Experimental Design:
384 BAP samples were sequenced to an average depth of ~25x.
Shotgun sequencing (127-bp paired-end) was done using an Illumina X10 instrument at the HudsonAlpha Institute for Biotechnology.
Variant calling was done using a computational pipeline at the Danforth Center.
See the Data Products page to get access to raw and derived data products.
Experimental Design:
768 RIL samples were sequenced using a GBS approach.
Season 1 sorghum (April - July 2016) Season 2 sorghum (August - November 2016) Durum wheat (January 2017 -
Three hundred thirty one lines were planted in Season 1.
Under scanner system
West of scanner system
The Lemnatec Scanalyzer Field Gantry System is the largest field crop analytics robot in the world. This high-throughput phenotyping field-scanning robot has a 30-ton steel gantry that autonomously moves along two 200-meter steel rails while continuously imaging the crops growing below it with a diverse array of cameras and sensors.
Twelve sensors are attached to the gantry system. Detailed information for each sensor including name, variable measured, and field of view are available here. The planned sensor missions and their objectives for 2016 are available here.
emergence vigor emergence final stand counts plant heights node and tiller counts on marked plants phenology growth stage data leaf desiccation ratings radiation interception managements Incomplete harvest yield data
One hundred and seventy-six lines were planted in Season 2.
Under scanner system - same as season 1
same as season 1
plant heights managements emergence vigor emergence final stand counts node and tiller counts on marked plants leaf length and width on marked plants, one date
Location: The Automated controlled-environment phenotyping at the Donald Danforth Plant Science Center Bellwether Foundation Phenotyping Facility
The consists of multiple digital imaging chambers connected to the Conviron growth house by a conveyor belt system, resulting in a continuous imaging loop. Plants are imaged from the top and/or multiple sides, followed by digital construction of images for analysis.
RGB imaging allows visualization and quantification of plant color and structural morphology, such as leaf area, stem diameter and plant height.
NIR imaging enables visualization of water distribution in plants in the near infrared spectrum of 900–1700 nm.
Fluorescent imaging uses red light excitation to visualize chlorophyll fluorescence between 680 – 900 nm. The system is equipped with a dark adaptation tunnel preceding the fluorescent imaging chamber, allowing the analysis of photosystem II efficiency.
The LemnaTec software suite is used to program and control the Scanalyzer platform, analyze the digital images and mine resulting data. Data and images are saved and stored on a secure server for further review or reanalysis.
Duration: 10 days on LemnaTec platform
Experimental Design:
3 replicates of were grown in a randomized complete block design
Watering regimes = 30% FC and 100% FC
Drought conditions were imposed 10 days after planting
Plants were imaged daily for 10 days (11-20 DAP) and sampled at 20 days after planting
Experiment was repeated twice to phenotype the full BAP (Reps 1A and 1B)
see
Experiment
Reps
Treatments
Experimental design
BAP
3
30 lines (12 PS, 12 sweet, 6 grain)
RCB with sorghum types nested in groups
Night illumination
3
5 illumination levels x 2 PS lines (with check line separating illumination levels)
RCB
Row #
3
6 adjacent plot scenarios: 3 lines (forage, sweet, PS) x 2 sides (east or west)
RCB but not balanced with all treatments in all reps
Biomass
3
5 sampling times x 3 lines (forage, sweet, PS)
RCB with sampling time as a repeated measure
Density
3
3 densities (5, 15, 30 cm) x 3 lines (forage, sweet, PS)
RCB
RILs
3
130 RILs plus 10 repeats of a single line/rep
Incomplete Block (row-column alpha lattice design)
Uniformity
17
2 lines (forage, PS)
None - Same line planted in single range
Experiment
Reps
Treatments
Experimental design
BAP
1
30 lines (12 PS, 12 sweet, 6 grain)
None - single rep planted for observation
RILs
3
60 RILs
Incomplete Block (row-column alpha lattice design)
