Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
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#
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.
The following protocols have been contributed by TERRA-REF team members:
Field Scanner - Coming 2017
Genomics - Coming 2017
A template for documenting protocols is available here.
SenseFly eBee fixed-wing drone
Hexacopter
UAV data are collected using one of three cameras:
5-band MicaSense RedEdge
4-band + RGB Parrot Sequoia
SenseFly thermal thermoMap
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).
Standard flight altitude is 44m with 75% image overlap (both sequentially and laterally), and missions are programmed and managed by either Mission Planner or senseFly eMotion.
No radiometric calibration was conducted as of Nov 5, 2016.
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 geospatial information).
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.
MicaSense: https://www.micasense.com
SenseFly https://www.sensefly.com
QGIS https://www.qgis.org
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.
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
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.
