The Bronson Files, Dataset 6, Field 13, 2014

Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and 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. This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform. The Hamby 6000 rig Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560 Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options. The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply. Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson. 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. 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 120 cm from and flush facing 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 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. Noting that 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 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 easier to direct, and in our capture method we target a consistent sensor height that is 1 m 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. 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, creating a discrete data row for each individual sensor measurement instance. We offer five high-throughput single pixel spectral colors, recorded at 530, 550, 590, 670, 730, and 800nm (NIR). The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including increased noise). Importantly, two green frequencies are available in this study, which is different from the alternate focus on the other side of the spectrum in the first two Bronson Files datasets measuring cotton. Dual, or tandem, CropCircle sensor paired 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) On collection 7/28/2014 and thereafter, a new HS logger, the GeoScoutX, or GSX, was initiated. The upgraded data recorder increased operational reliability by eliminating recording stops and subsequent multiple data files. The new data outputs were defined by the operating system configuration version where the data variables column headers were changed to be named SF00, SF01, SF02, SF03 and SF04. The raw reflectance columns are the first three, SF00-02, and the last two columns are the onboard calculated VIs, which we did not consider. 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. 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. This second data component, expanding measurement using Campbell Scientific records and additional sensors was added during the season. However unfortunate, the CS data of this dataset is of poor quality. The IRT sensors that were positioned in a nadir and forward view were coded at the wrong bit-depth regarding their A to D channel, and data was extremely down-sampled as a result; to the point where it was of no value to us. There was an active shaded thermocouple junction that gave a reasonable ambient temperature and likely represents the best thermometric recorded. This initial Hamby CS application was developed concurrent to continued proximal sensing cart operations and other research projects. Ultrasonic canopy height data exists for the six CS data collections. With caveat that as part of our middle term development, it was derived using the less-expensive MaxBotix lower-frequency sensor that we subsequently found to be problematic. Later the sensor was fully replaced by the Honeywell product. At first we thought that the MaxBotix ultrasonic sensing technology line, which was successful in the marketplace sensing snow levels, might also be able to sense plant in-field canopy displacement, because both targets exhibited soft irregular surfaces. Related initial testing using regular target shapes of various sizes and angles in the controlled space of a shop showed reasonable performance. Indeed, in the field operations, of products ranging around the 50kHz frequency, the MaxBotix signal return was good, as it returned perhaps 50% acceptable data on average, rather than what could be even less. We also found a minor transient ground noise issue on one unit. We did consider operating multiple MaxBotix sensors in a cluster and sample the same area multiple times, so to give multiple chances for a proper reflection to be recorded. This approach did improve data quality in field testing, but we never achieved the performance we wanted in measuring the cotton leaf canopy target; and eventually we discontinued use. To illustrate the marginal displacement return, further description of the common ultrasonic sensor errors encountered is given in a file, Ultrasonic data signal report, with labeled time series charts of the weak CS MaxBotix data from this dataset. CS data contains accurate IRT sensor body temperatures, the electronic panel temperature, and GPS information. Due to the weakness of the CS collections, they are absent from SAS generations of intermediate tables as Dr. Bronson did not find them informative, beyond the marginal height signal. Plant heights were measured by hand to include both bed and furrow components, and data for one day of measurements are included as a separate file, Manual height data. 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. However data is generally in very good condition, tabulated and annotated, with the inclusion of intermediate analysis formula, and laboratory test results. Summary: Active optical proximal cotton canopy sensing spatial data and including few additional related metrics and weak low-frequency ultrasonic derived height 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 2014 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 the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional ultrasonic data signal explanation is offered as annotated time-series charts. 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 were 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).