Dataset Title: The Bronson Files, Dataset 5, Field 105, 2014

Citation: Bronson, Kevin; Conley, Matthew M. (2021). The Bronson Files, Dataset 5, Field 105, 2014. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1523390.


EXPERIMENTAL DESIGN AND OPERATIONAL DETAILS

Experimental design and operational details of research conducted are contained in related published articles, however further description of the measured data signals as well as germane commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. 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 120cm from a titanium dioxide white painted panel, a normalized unity value of 1.0 is set for each detector. To generate this dataset we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

This type of 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.

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 each view. It does however, not represent a reflection of the plant material solely because it can contain additional features in 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.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are more easy to direct, and in our capture method we target a consistent sensor height that is 1m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

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 as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version. Fifteen collection events are presented.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost. Multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used, supplying data in a lined format, where variables are repeated for each sensor. This created a discrete data row for each individual sensor measurement instance.

We offer six high-throughput single pixel spectral colors, recorded at 530, 590, 670, 730, 780, and 800nm. The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including an increased noise).

Dual, or tandem approach, CropCircle paired sensor usage empowers additional vegetation index calculations, such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Improved Campbell Scientific data at 5Hz is presented for nine collection events, where thermal, ultrasonic displacement, and additional GPS metrics were recorded. Ultrasonic height metrics generated by the Honeywell sensor and present in this dataset, represent successful phenotypic recordings. The Honeywell ultrasonic displacement sensor has worked well in this application because of its 180Khz signal frequency that ranges 2m space. Air temperature is still a developing metric, a thermocouple wire junction (TC) placed in free air with a solar shade produced a low-confidence passive ambient air temperature.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

There is canopy thermal data from three view vantages. A nadir sensor view, and looking forward and backward down the plant row at a 30 degree angle off nadir. The high confidence Apogee Instruments SI-111 type infrared radiometer, non-contact thermometer, serial number 1022 was in a front position looking forward away from the platform, number 1023 with a nadir view was in middle position, and sensor number 1052 was in a rear position and looking back toward the platform frame. We have a long and successful history testing and benchmarking performance, and deploying Apogee Instruments infrared radiometers in field experimentation. They are biologically spectral window relevant sensors and return a fast update 0.2C accurate average surface temperature, derived from what is (geometrically weighted) in their field of view.

Data gaps do exist beyond null value -9999 designations, there are some instances when GPS signal was lost, or rarely on HS GeoScout logger error. GPS information may be missing at the start of data recording. However once the receiver supplies a signal the values will populate. Likewise there may be missing information at the end of a data collection, where the GPS signal was lost but sensors continue to record along with the data logger timestamping.

In the raw CS data, collections 1 through 7 are represented by only one table file, where the UTC from the GPS NEMA RMC string was parsed and passed as a variable. For collections 8 to 15, there were two CS raw data tables produced. From collection 8 on 3/6/2014 onward a more stable version of collecting the raw GPS information was instituted, where the full raw string was recorded at 1Hz. The Hemisphere GPS receiver A100 (or A101) was used. This enabled all the information from the two GPS strings (RMC and GGA) we were producing on the receiver to be recorded fully, while still allowing the main data table its 5Hz resolution recording for the phenotyping sensors.

Before collection 11, on 3/26/2014, CropCircle raw detector filters were moved, affecting raw data ordering not represented in the GeoScout recording system. There was a quality concern and Dr. Bronson choose to move the filter position on the sensor to ensure quality was retained across the season.

Before collection 12, on 4/1/2014, CropCircle filters were moved again, affecting raw data ordering that is not explicitly represented in the GeoScout recording system. Again Dr. Bronson choose to move filters to ensure no bias was present or otherwise to optimize colors of highest interest on apparent best performing detectors. A calibration room characterization of CropCircle ACS-470 sensors was conducted, but data is not reported.

Originally the filter set was for Sensor#2 was, R1 = 530, R2 = 730 and R3 = 670, that was changed on 3/26/2014 to be, R1 = 530, R2 = 670 and R3 = 730, and then changed again on 4/1/2014 to be, R1 = 530, R2 = 670 and R3 = 780; likewise Sensor#3, which was originally R1 = 800, R2 = 780 and R3 = 590, was not changed on 3/26/2014, but was on 4/1/2014 to be R1 = 800, R2 = 730 and R3 = 590, where the RedEdge filter from Sensor#2 supplanted the 780 NIR, and vice versa.

Note, when re-processing raw HS data, be sure to handle these filter shuffle changes. SAS processed outputs have already included the changes and do not need additional handling.

On 4/7/2014 to 4/10/2014 there was a NSF-funded phenotyping workshop with Kansas State University, where the Proximal Sensing Cart platform example was offered to the research community.

SAS output files represent intermediate pre-processed tables, where variables are ready to be mapped or statistically analyzed. There may be a few instances of serial communication numeric or other errors not replaced by the -9999 null term in the intermediate files, however this does not apply to the phenotyping sensing variables, only GPS and time variables which were still being developed in minor ways by developing CRbasic code on the CS logger.

SAS output files were generated by Conley, after the data collections and throughout the season, where Dr. White mentored, and in collaboration with Dr. Bronson and Dr. Mon, these intermediate table outputs were produced and delivered internally. Although minor formatting was performed for clarity on the files presented here, all the original table information was retained, as much as possible, to example process and provide the same data for reprocess.

The MegaTable file is a version of Dr. Bronson’s intermediate analysis table. Therein is a list of variable names with their descriptions, although a few variables have not been full described. Additional soil fertility chemistry information and calculated values are also presented, and the file also serves as an example.

The PSCM1 cluster bracket positions our sensors at one location, such as underneath one GPS receiver, effectively sampling at one spatial point within the base granularity of our subsequent data model. This approach avoids the need for assigning sensors different receiver offsets. We centered tandem orientation CropCircle sensors with the ultrasonic and the thermometry centered on either side. We successfully applied high-throughput plant phenotyping electronic sampling in the field, as understood through a coordination of spatial dimension, or areas in 3D space. GIS was successfully utilized to initially plot, annotate, analyze and report the data in an investigative and often iterative fashion.

Data is generally in very good condition, tabulated and annotated, with the inclusion of intermediate analysis formula, and laboratory test results.