Dr. Kevin Bronson provides a dataset representing the third of three consecutive years of cotton and nitrogen management experimentation in Field 113 of the Maricopa Agricultural Center, Arizona USA. Included is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.
Experimental design and operational details of research conducted are contained in related published articles, however a further description of the measured data signals and commentary is herein offered.
This Hamby platform third year of F113 cotton experimentation includes a large utilization of nitrogen-15 isotope tracing to support evaluation of nitrogen use and uptake. The UC Davis Stable Isotope Facility (SIF - https://stableisotopefacility.ucdavis.edu/) provided laboratory analysis of samples to determine isotope percent recovery. Typical nitrogen fertilizer was delivered as liquid UAN 32-0-0 fertilizer with a density of 11.1 pounds per gallon, which contains 3.5 pounds of nitrogen per gallon.
GeoScoutX logging of CropCircle active optical reflectance sensing data -
The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 generated reflectance numbers. Which as derived here, consists of raw active optical band-pass values digitized onboard the sensor product. Data was delivered as sequential serialized text output including the associated GPS information. Typically, this product examples a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. However, we used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product, being not only rugged and reliable, but illumination active and filter customizable.
Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120 cm from and flush facing to a titanium dioxide white painted panel, a normalized unity value of 1.0 can be set for each detector. To generate this dataset, we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalization) on each sensor individually, before each data collection, and without the use of any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows PC machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.
Noting that this type of raw value active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system. We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically while onboard the “black-box” device, than are other reflectance products which produce vegetation indices that are averages of multiple detector samples.
Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within the field view. It does not represent a reflection of the plant material solely, because it can contain additional features in the view.
Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI; because the active light source does not transmit energy to penetrate perhaps past LAI 2.1, which is less than what is expected with a solar induced passive reflectance sensor. However, the focus of the active sensor scan is orientated on the uppermost expanded canopy leaves, and those leaves are normally positioned to intercept the major of incoming solar energy. Active energy sensors are easier to direct, where this capture method targets a consistent sensor height of 1 m above the average canopy height, and a roaming travel speed maintained around 1.5 mph, with the sensors parallel to earth in a nadir view.
Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors that were measured on the GeoScoutX digital propriety serial data logger, have a stable output format as defined by firmware version.
Different numbers of csv data files were generated based on field operations. Raw data files include the inserted null value placeholder -9999. CropCircle sensors supplied data in a lined format, where variables were repeated for each sensor creating a discrete data row for each individual sensor measurement instance.
There are four ACS-470 active optical sensors utilized, which create a dual tandem left and right side two crop row setup. These sensors are numbered 1, 2, 4 and 5, where sensors 1 and 5 have filters 550, 670, and 530, while sensors 2 and 4 have filters 590, 800, and 730, each for their respective R1, R2, and R3 raw data channels.
White panel measurements were conducted, using a large 4x8’ plywood sheet rough surface painted white (Behr flat ultra-pure white #1850, ingredient titanium dioxide) where the CropCircle sensors were positioned 120cm above the white panel and data was recorded before (pre) and after (post) field collection measurements. Not only did we field normalize all the CropCircle ACS-470 sensors to a unity value of 1.0 before the field collection event via this setup and the company’s supplied communication interface software (SC-1 hardware), but we were able to also measure subsequent minor detector offset and noise present in the individual raw data streams. Likewise, after the data collection we were able to quantify any apparent sensor drift in the post collection measurement. This pre and post collection white panel raw data is available in the HS dataset and allows for adjustment of raw values.
CropCircle raw data adjustment approaches –
Three undescribed adjustment value test calculation data columns are included, appended to the original raw data tables. For each CC sensor detector, the white panel observed amplitude delta of the raw reflectance channel was used to create minor data adjustments. This calculated test value was appended to the raw data table as variables R1_adj, R2_adj and R3_adj, and example a possible raw data minor adjustment.
This was the beginning period of a method advancement, in testing control based normalization adjustments to raw active optical detector data values. Generic and course post-process raw data adjustments can be made by first measuring a white panel reference at 120 cm distant, before and / or after a data collection period, which is beyond using only the SC-1 device to normalize individual sensor detectors. A deviation from the flat white reflectance typical 1.0 unity value was recorded and used to offset the detector raw radiance values.
The raw data adjustment test approach was developed as an extension of the manufacturer’s normalization routine recommendation, which uses the SC-1 device or a titanium dioxide ultra-white painted custom panel. Normally, the ACS-470 detector channels would be set to read 1.0, after 30 minutes of warmup time and when connected to the SC-1 illumination reflector, or when held 120 cm away from and facing an optically flat white panel of sufficient size to fully reflect the active light signal footprint (about 30 x 100 cm). This recommended approach does work well.
We normalized multiple sensors in a field condition, by using the typical two tailed white panel field-normalization approach. One by one, each sensor was connected to the SC-1 box for communications with a PC, where the sensor real-time information was viewed and a sensor normalization command given. Once placed at an appropriate height and position relative to the white panel, a sensor zero point as measured was ascribed to the sensor configuration, by first covering the active optical LEDs source and detectors and creating a black-out condition, and then immediately afterward revealing the illuminated white panel in full detector view where a second full signal measurement was made and the unity 1.0 set point value instructed to the sensor.
Values streaming through the active optical sensor detectors typical range 0-2% around the unity value after field normalization, while in a natural condition measuring the course surface white control panel. Therefore successful normalization was deemed to have occurred, or was not needed, when all detectors were within 2% of the 1.0 value when using the white panel setup. It was difficult to achieve a 1% data range for all the detectors at all times, where multiple iterations of the normalization routine would not consistently yield improved results of 1% magnitude.
Therefore, we simply measured the typical 0-2% raw data value difference for each detector, with the idea that a subsequent adjustment may be possible. We found that we could measure longer time periods with sensors over a white panel reference and determine optical signal features, as well as elucidate individual sensor minor behavior. Temperature change apparently induced effects on the raw detector data stream. We also recorded the sensors when connected optically to the SC-1 device reflector, in dark conditions, at various distances and angles from a target, and with many different types of target reflectors, in a temperature control room, laboratory, shop and outdoors.
We noted that each detector of each sensor can exhibit unique behaviors, which underlie the customizable band-pass color filter’s effect. Some detector channels increased with increased temperature but most decreased. The raw data magnitude shift, including the filter, was typically 0.3% per degree C, yet it was variable. However these detector behaviors were stable and repeatable. Therefore the following initial test method adjustments were considered.
Adjustment approach #1 was a pre-collection white panel based adjustment. This is the most common approach, where after a period of sensor warming and after the signal data stream fully stabilized, the typical 0-2% average sample difference from the unity value 1.0, was added back to the raw values of each detector, before the VI calculation. Understanding that proximity drives signal amplitude, the 1.2 m distance from panel to detector during normalization is related to the 1 m above average canopy height in the field.
One color small raw detector channel possible offset bias effect can either compound or mitigate VI error, based on the second channel color detector possible offset bias. In practice, mitigation was more typical, where detectors that were to be normalized in ratio drifted in the same direction at the same time, although detector drift was usually not to the same magnitude. An expected VI calculation drift might be 0.1% per degree C.
Adjustment approach #2 was a post-collection white panel adjustment. Where the average sample difference from unity, as measured reflectance from the white panel at the set height and after the field data collection, was simply added back to each of the raw detector values.
Adjustment approach #3 involves one average value, or a linear interpolation of the pre (before) and post (after) field collection white panel measurements. Measuring the white panel before and after a field collection allows for improved adjustment potential, by creating a condition of bookends of control measurements around the experimental field measurement.
There was no need to stop log between white panel measurement in full sun and the field collection or the sensor’s return to its white panel position. Rather one continuous data log can be useful to validate data quality features outside of the experimental or control recordings.
The initial sensor warmup consideration was important, because the active light source created a somewhat spherical heat artifact that originated on the LED source, and that spread across the detector physical area and throughout the rest of the sensor body. Although perhaps 90% of the heating occurs in the first 30 minutes of a calm condition warmup, a minimum full hour was operationally favored to achieve closer to 95% warming, or rather three hours warmup time to a 99% thermal effect and best condition achievement. The goal is holding a consistent sensor temperature. Furthermore, sensors that are in full sun condition will warm quicker and become warmer than those in the shade. Fully warming sensors within solar impingement and then normalizing them just before a field collection gave the best thermal condition result.
Regarding thermal condition after a field collection, a measured white panel with the CC sensors in full sun gave a more accurate thermal data condition, than when the rig was returned to the shade of a garage where the temperature of the sensors would drop a few degrees rapidly lowering the sensor thermal body status from where it had been in the field.
There is an environmental or weather consideration; typically we encountered ambient warming during the field collection time period, which would boost the sensor thermal value after the pre collection white panel measurement. Conversely, a cool breeze event would cause a relatively rapid sensor body temperature decrease. We were even able to measure minor solar thermal loading on the sensor body that would change with rig orientation (data not presented). Obviously a sensor in the sun will be warmer than one in the shade. A passing cloud or shade event can change sensor body surface temperature and propagate a weak thermal pulse to the detectors.
We wrapped sensors in external insulation, to reject abrupt external changes in temperature and smooth the thermal change effects that did occur. Insulation apparently mitigated our transient solar loading influence and the cool breeze effect.
The post collection adjustment approach worked well for instances where sensors came to their highest temperature early in the experimental collection, soon after encountering the full sun and field conditions.
Adjustment approach #4 takes into consideration ambient and equipment temperature measurements to select whether the pre (before), post (after), or both control values could best adjust for mitigating minor thermal artifacts, and at which periods of the field data collections those artifacts may have occurred.
Pre and Post field data collection datasets are available for the CC sensors, presented as extra csv files which depict white panel unity sampling. Data shows raw channel static reflectance signal quality.
Noting that the adjustment approach is technical in nature and ancillary to the primary research investigation. Following the CC manufacturer’s original guidance was considered sufficient for typical operation. The adjustment values are offered as example only, while the primary raw data values are presented as they were originally recorded. The adjustment values were appended as part of the original GSX processing and constitute actual research process values that were considered by Dr. Bronson at the time of investigation. Therefore, use the R1, R2 and R3 variables from the HS raw data table to access the original data, rather than the white panel secondary adjusted test values.
Additional 12v power supply switches were installed, to allow separate control of power to the two active sensor arrays. This helped in supporting pre-collection sensor warmups and otherwise running the system without energizing the active optical reflectance component.
Although the GeoScoutX handset includes a GPS receiver internally, and can record its own geo-located data stand-alone, it also allows incorporation of a separate GPS signal. We connected one Hemisphere A100 GPS receiver to both HS GeoScoutX recorders and the CS CR1000 data logger, all on the same cable, so they would all receive the same GPS time and location information at the same time.
Campbell Scientific CR1000 logger data -
As part of investigating the integrity of the primary dimensional annotation geo-location data, two additional parsed GPS variables were highlighted for observation. The number of satellites in view, and the signal quality, are new variables added to the CS table, because there was a question regarding instances of transient data apparent signal accuracy degradation. We expected a 30 cm accuracy on the ground, and typically that was the case. However there were instances where the location (not time) calculation drifted outside that window, perhaps to 50 cm, or became in error. The WAAS correction, or whether a local interference may have occured, even a solar flare event was considered, but no firm conclusion was met.
The Hemisphere GPS receiver output followed RS232 serial communications protocol and involved DB9 format cable connections. NEMA RMC and GGA strings were recorded at 5 Hz. The RMC and GGA UTC variables were viewed as dual time recordings and became the primary key coordinate data table values. Raw GPS string variables were recorded in a redundant fashion so that if one variable was missed, the other might still be present. Sometimes the CS logger had trouble decoding every serial string transmitted. If start and stop characters were not recognized within 200 ms of program time, the string variable may have returned NAN and the memory pointer moved and or memory buffer cleared. Therefore, a more CPU compliant and stable approach of recording the GPS string term as a single variable in the data table proved better to allow more complete CS based GPS NEMA string comma separated data recordings. These text strings can be parsed in post processing.
An HC2S3 air temperature and relative humidity sensor with radiation shield was added to supply roaming ambient energy status. Roaming environmental sensors alongside phenotyping sensors is a fundamental method action to better resolve transient and more subtle dynamics at smaller granularity. Weather station micro-meteorological measurement is valuable in plant management, selection sampling and simulation modeling. However this further localized measurement of the micro-meteorological environment directly above the plant canopy being investigated, and in coordination with other related field measurements, can provide improved value in understanding localized or transient events or effects.
The addition of data from a solar pyranometer (RAD) sensor on top of the Hamby rig and recorded on the CS logger starting 05/02/2017, which allowed incoming solar energy at the measurement location to be more accurately characterized. If a partly cloudy condition were to occur, one inducing solar illumination change on the ground, then thermal surface target measurements can be greatly influenced by the change in incoming energy. Not only does measurement of the incoming solar energy provide a basis for biological productivity calculations, it also provides an energetic context for adjusting, explaining, or otherwise handling the experimental non-contact thermometry samples.
Recording of error flags on the CS logger was done to support user feedback, where the user would be signaled by a buzzer when a data parameter was out of bounds. The user could see which of the primary metrics was out of bounds and the number of program iteration flags generated by looking at the logger LCD where real time data tables were visible. Later in the data quality control action, post collection processing of the total number of flags and the times of their occurrence were evaluated along with the data samples.
Taken together as a modular data package, the GeoScoutX and Campbell Scientific logger, were connected to the same GPS receiver, and positioned with keypad interfaces and displays near the rig operator’s view.
A 100 watt solar panel was attached above the operator’s roll-cage shade plate to provide 12 volt power boost during field collections. This can be useful in cases where batteries are drained unexpectedly or have reached reduced capacity near end of life. Having solar power generation supplying a roving data system is recommended to support full battery power status during the important field operations.
There are instances where system data was recorded before field collection as a warm-up period, or after data collection, as well as during travel to or from the field. This ancillary data was used to support quality control and was not part of the primary experimentation.
Specific instance data notes -
The CS data RMC_UTC_Az variable is not valid, it was used for an onboard local time display and was not data typed correctly to be recorded to the final data table, therefore disregard this variable and always use standard GPS based UTC as the time variable.
Note that the GPS NEMA information was recorded as GGA and RMC comma separated string variables. These strings are to be parsed to access the individual GPS variables such as location and time.
Intermediate SAS generated data tables may have small gaps which are not present in the raw data.
04/23/2018 The HS dataset has no pre or post collection white panel measurements. There is additional later time period optical data in the intermediate table, not present in the raw data table, where it appears the HS logger was run independent of the CS after the initial collection.
Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented.
Agronomic nitrogen and irrigation management related field operations are listed.
Unique research experimentation intermediate analysis table is made available, along with raw data.
The raw data recordings, and annotated table outputs with calculated VIs are made available.
Plot polygon coordinate designations allow a re-intersection spatial analysis.
Data was collected in the 2018 cotton season at Maricopa Agricultural Center, Arizona, USA.
High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig.
Acquired data conforms to location standard methodologies of high-throughput plant phenotyping.
The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake was also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).
- Data Dictionary - The Bronson Files, Dataset 10, Field 113, 2018csv Dataset data dictionary
A comma separated file is presented, representing a table of the primary raw...MD5:Explore Data6.06 KB
- Field Activities Log - The Bronson Files, Dataset 10, Field 113, 2018csv
A comma separated file is presented, representing a table of experimental...MD5:Explore Data5.45 KB
- MegaTable - The Bronson Files, Dataset 10, Field 113, 2018xlsx
A unique full experiment season intermediate analysis Excel workbook is...MD5:Explore Data866.47 KB
- Intermediate analysis tables collection - The Bronson Files, Dataset 10, Field 113, 2018zip
A collection of 11 table files in csv format is presented, representing 11...MD5:Explore Data55.33 MB
- The collection of raw active optical reflectance csv files from the HS data loggers - The Bronson Files, Dataset 10, Field 113, 2018zip
This HS data zipped folder, contains the 31 raw active optical reflectance...MD5:Explore Data28.09 MB
- The collection of raw environmental acquisition csv files from the CS CR3000 data logger - The Bronson Files, Dataset 10, Field 113, 2018zip
This CS data zipped folder, contains the raw environmental and target...MD5:Explore Data21.52 MB
- Treatment and harvest plots corner points coordinates - The Bronson Files, Dataset 10, Field 113, 2018 Cottonzip
Four comma separated files are presented, containing plot water treatment...MD5:Explore Data19.43 KB
- Cotton petiole nitrogen guidance chart - The Bronson Files, Dataset 10, Field 113, 2018 Cottonpdf
A figure relating cotton petiole nitrate in ppm with growth stage is...MD5:Explore Data173.31 KB
|Release Date|| |
|Spatial / Geographical Coverage Area|| |
POLYGON ((-111.97890043259 33.080087064259, -111.97890043259 33.082424376658, -111.97701215744 33.082424376658, -111.97701215744 33.080087064259))
Ag Data Commons
|Spatial / Geographical Coverage Location|| |
Maricopa Agricultural Center farm Field 113
|Temporal Coverage|| |
December 12, 2017 to November 27, 2018
|Contact Name|| |
|Public Access Level|| |
|Program Code|| |
005:040 - Department of Agriculture - National Research
|Bureau Code|| |
005:18 - Agricultural Research Service