The Bronson Files, Dataset 7, Field 13, 2015

Dr. Kevin Bronson provides a second experiment year of Field 13 nitrogen and water management in cotton agricultural research data 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. The reflectance data is good. There are some errors in the CS data. 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 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, 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) 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. Active air temperature sampling was conducted, by measuring an aspirated thin wire thermocouple junction housed within an insulated radiation rejection shield. The sensor was positioned at nominally 50 cm height above the plant canopy. The sensor was placed alongside the two infrared radiometers with its air intake facing downward. Air temperature that is measured in this way may better resolve the plant ambient growing condition air temperature; and here it is measured at the same time as the IRT surface temperature measurement. The high dynamic nature of this active bulk thermal trace allows minor fluctuations in local sensible temperature to be recorded and compared with the other metrics. The plant phenotyping proximal active air temperature sampling approach, is a method that achieves measurement spatial position close to the plant or soil, or at a specific distance there from. The increase in spatial precision possible, and including the location frequency option, is a technical feature that could support generation of simulation models or validation tests. https://www.researchgate.net/publication/327212846_Phenotyping_applicati... One Honeywell and two MaxBotix sensors were used to measure plant displacement in the field, using ultrasonic transduction reception sensing. Two Maxbotix sensors were run as a pair, to test if signal quality could be improved by increasing the number of samples collected, through a timed dual sampling within the 200 ms program loop interval. The ultrasonic data showed a superior quality return from the single Honeywell sensor, primarily due to its higher 180 KHz sound frequency characteristic. The Honeywell sensor signal data return was easier to interpret; it also includes a maximum value error designation output flag as a normal function. A VectorNav VN-100 AHRM unit was tested, as a way to better resolve kinematic spatial and physical platform elements of motion. Rig heading was of primary concern as the calculated GPS course variable has no rig facing orientation information. The platform yaw reference measurement allows other sensors to be positioned correctly if for instance travel is in reverse. Although the micro-mechanical product provides a robust technical solution, it was not implemented correctly for this dataset. The magnetic model measurement and correction was not installed to the device, and consequently the important yaw variable was not resolved. The other sensor data was not considered during the experiment. There were instances of missing GPS information, mainly due to mishandling of string data in the CS application. There were also GPS data gaps due to mechanical failures, such as a wire disconnection. The raw HS data contains a second recording of the original GPS receiver information. As the season developed, serial string data was collected as a single variable, to reduce compute overhead. The process did stabilize and become a single file CS output as its final form. Intermediate files that were output from SAS process near the time of data collection, may contain a corrupted CS timestamp. Where the long string format timestamp variable did not parse correctly or was format corrupted after process. Rather use the UTC time variable as the true time coordinate in all instances, and in the intermediate files. The original CS logger timestamp in the raw data is good and most precise, but it may have drifted from absolute true time. Intermediate files may contain additional variables that were calculated as tests and do not represent the original experimental returns of the raw data. There are instances where the CS data is missing from the intermediate file. This occurred likely because the SAS program used was not updated to accommodate a changed data input and so the two logger generated data streams did not get table merged. CS data if available but not in the intermediate file, will be preserved in the raw CS logger output files. 06/08/2015 GPS string corruption issue resulting in gaps. This was a problem for us as it caused an effective data loss anytime our base location and time information was missing. The location data for CS raw data is intact. It could have been possible to interpolate location and reconstruct measurements, however we did not attempt salvage data in this case. It is interesting to note that the forward view IRT resolved the plant signature better than the nadir, which is the uncommon state. 06/24/2015 GPS is almost good, showing a few gaps. Again the CS GPS location data is intact. The MaxBotix sensors are failed for this collection. It looks like a power supply or wire connection issue. It is interesting to note that the nadir view IRT resolved the plant signature better than the forward, which is the common state. The idea of tilting a non-contact thermometer into a forward angled view of perhaps 30 degrees off nadir is intended to increase the percentage of vegetation in sensor view along the crop row planting. The sensor would look more onto the side view of a small plant in the early condition before canopy closure, but there is also an increase in physical distortion in the data. Here a transition in growth form effect on the thermal signature characteristic, between this and the pervious field collection, is visible in the IRT data. Thermal data from these two field collections suggest an elucidation of the thermometry view optimization application. Although there is a large volume of comparative data collected from other platforms and experiments, the results regarding this view method are largely undescribed and a nadir only view is typical. 07/13/2015 CS data not present in intermediate file. CS output becomes one file format. 07/14/2015 The intermediate file contains recording of the travel period from research center to field and back. However there is a big gap in the CS data about mid-way through the collection. 07/22/2015 CS data not present in intermediate file. 08/05/2015 Intermediate file contains the travel again. From this point forward the SAS output finally became stable as a process. In order to solve transient connection issues that are usually due to a faulty connector, all cables were inspected closely, where connectors were reset or replaced and eventually we corrected the errors. Data gaps do exist beyond null value -9999 designations, there are some instances when GPS signal was lost. However data is generally in very good condition, tabulated and annotated, with the inclusion of intermediate analysis formula, and laboratory test results. 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. Note that plot polygon coordinate points are unchanged, as presented in the first year dataset. https://data.nal.usda.gov/dataset/bronson-files-dataset-6-field-13-2014/... Map image https://data.nal.usda.gov/dataset/bronson-files-dataset-6-field-13-2014/... UAN nitrogen fertilizer 32-0-0, 11.1 lbs/gal density, 3.5 lbs/gal N Summary: 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 2015 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 the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional data illustration is offered as a report file with 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).