Maricopa Phenotyping

Using a lightweight and narrow wheeled electric-drive field proximal sensing platform, local plant and environmental measurements of cotton and other crops were collected on the Maricopa Agricultural Center farm as part of the Arid Land Agricultural Research Center’s plant phenotyping program. Beginning in 2012, the single pass continuous scan high frequency approach to measure agricultural crops has developed into sensing the analog tri-metric of plant health status, which consists of canopy temperature, canopy height and canopy spectral reflectance. The method also captures environmental micro-energetics such as vertical air temperature and humidity profiles, incoming solar radiation, plus wind speed and direction. Positioning additional sensors in a low mounted and side angled view has also complimented the typical above canopy nadir sensor views. Fusing of the metrics has resulted in single term descriptive calculations and vegetation signature indexes. Color imagery has provided inputs for plant emergence timing, cover estimations, and relations of vigor, as well as providing input for structure from motion surface and volumetric modeling. Taken together, the data rich amalgamation of proximal measurements offers a viable method for biological discovery, physically positioned between hand-collected and unoccupied aerial derived sampling.

High-throughput plant phenotyping in field conditions using proximal sampling is useful to support agricultural research by the encoding of plant genetic response to environment and management influence. Proximal Sensing Carts employed by the USDA in Maricopa Plant Group Phenotyping Team have successfully measured multiple plant traits and environmental variables in field conditions by typically focusing on a two-meter bounded volumetric scanning measurement space. The presentation, “F113 2021 Wolverine Run4 Data Image Review”, shows a phenotyping method process applied to a short turf grass, where we collected initial test data to aid research experiment development and method tuning. Included are examples of sensor data returns, where the sensor performance and associated context are visible. Importantly, examples of GoPro Hero7 Black action camera imagery are presented. The limit of effective image geometric and color resolution was examined to verify if the inexpensive camera technology would be sufficient to compute plant cover area and height. Basically, results showed that plants and soil were geometrically characterized with about 1 mm ground sampling resolution in a single 2-meter distant image, but that optical aberration inherent between images and cameras with different views, resulted in spatial errors of about 2.5 cm across a 1 m plot space. Also, the pixel colors returned did show increased base spatial error beyond 1 mm, apparent at the edge of green leaf and brown soil transitions where color bleed-over and a yellow color artifact was detected. Of note is that image pixel saturation increase or color shift did not harm Metashape structure from motion model formulation. Instead, increased pixel color tended to increase the number of pairwise matched model tie-points.

Single-pass continuous high frequency two-meter proximal photographic sampling of agricultural plants grown in field conditions can resolve color and 3D geometric phenotypic traits, for example, if an adequate view angle, image overlap, and resolution are provided as input to modern structure from motion software. The USDA in Maricopa Plant Group Phenotyping Team presentation, “Summer Proximal Imagery Report”, notes an approach where multiple GoPro Hero 6 and 7 Black GPS action cameras were mounted to a moving Proximal Sensing Cart, and then set to continuously record plant canopy color and structure across more than one acre of two crop rows for cotton and soybean plants growing in experimental plots of the University of Arizona Maricopa Agricultural Center farm, USA.

Proximally sampled Structure from Motion multi-angular photogrammetric optical modeling has proven useful in architectural and physical archeological research. Yet in agricultural plant phenotyping the method is less mature, where the optimum camera view angle and distance from target to capture a complicated and often obfuscated biologic of living plant canopies in the field is less understood. The USDA in Maricopa Plant Group Phenotyping Team investigated several photographic capture approaches and found success in point-cloud modeling of cotton experimental plants growing forty inches apart outdoors, by using a single GoPro GPS action camera positioned above and beside rows of the crop plants. The presentation offered, “GoPro image captures for Metashape SfM point cloud modeling”, shows initial results, where the camera was manually moved around plants to test which view angle and how many images would be required to create color pixel tie-point models representing the cotton plants, as computed in Agisoft Metashape Pro software. Of interest was the side view reconstruction, which can be difficult to obtain from a typical more remote un-occupied platform aerial vantage. Additionally, camera views at ninety degrees to the plant rows resolved the understory fruiting bodies best in a single image, but they did not stitch together well and rather introduced unwanted background sky pixels.

It is common in contemporary environmental data acquisition, for a UAS platform to collect RGB type imagery of agricultural experimentation and provide data input to compute orthomosaic composites of field plots, or basic volumetric models via multi-angular structure from motion derived 3D color point clouds. Acres of ground may be sampled in minutes and results are generally good to support experimental biological targets. However, the resultant optical data constructions can be somewhat low resolution unless a robust and therefore often heavy and expensive camera is used which increases operational cost. Proximal terrestrial RGB image collection effectively offers high optical resolution by suspending an inexpensive camera close to the ground targets, at perhaps two meters distant. Closer proximity allows increased pixels on target, but it does require more images to generate the orthomosaic and cloud models of the experimental plot areas, and a subsequent increased time to cover the acreage. The USDA in Maricopa Plant Group Phenotyping Team presentation, “Multiple GoPros in nadir view SfM Metashape initial model compute Soybean”, is offered as example of our first field image collections from Avenger Rig in 2019 that showed high frequency proximal sampling viability in using inexpensive GoPro HERO 6 Black action GPS cameras and the Agisoft Metashape software to optically model a complicated soybean canopy across two acres of experimental plots. This image test method was conducted in addition to our regular roaming geolocated tri-metric of plant health status and environmental micro-meteorological phenotyping data acquisitions, and therefore compliments those analog point data streams with photographic color grids.

It is now somewhat common for simulation modeling in silico of agricultural plants to represent genetic, environmental, and managerial influence and compute a predicted vegetative biological growth construct. Yet to effectively create or validate the programmatic parameters employed, there is need for an accurate measurement of the environmental status within which the plants of known genetics are grown. Often a standard weather station is used to measure the micro-meteorological phenomena near the genotypic accessions, where a course time granulation of basic environmental information is attributed as input to the numeric construct. However, additional parameters at higher temporal frequency, that include soil and surface energetic status, and arrayed samples distributed in a vertical spatial profile may add to an energy balance and thermodynamic spatial understanding, and perhaps further support compute simulation. The USDA in Maricopa Plant Group phenotyping team presentation note, “EOS IRT stations F119 2021”, the third in a series of three, shows images and end of season example data from two modified weather stations recording soybean plants. The two stations each measured continuous 5Hz non-contact surface temperatures of a single row of plants from both a nadir and angled view, along with air temperature and humidity, solar radiation, soil moisture and temperature in the upper soil zone, and contact temperatures from several type-T thermocouple junctions placed in the soil at different depths and placed both on the soil bed and in the soil furrow. Photographic images and timeseries charts are presented to show some initial result raw data, and the method of measurement approach tested. This preliminary investigation involved a test setup of high frequency environmental and plant sampling that recorded most of the 2021 summer season via two station locations, each measuring the same soybean variety. The approach allowed generation of a dataset that included repeated measures of dynamic and changing plant growth conditions. Therein, multiple ambient sub-surface wetting and weather events resulted in substantial thermal and latent heat fluctuations. These patterns appeared to resolve micro-environmental and phenotypic differences likely to support crop growth simulation modeling. Measurements were collected in the spatial and temporal context of related roaming proximal phenotypic platforms, UAS derived imagery, and robust physical sampling, among other experimental parameters. We considered adding additional metrics to the IRT stations, such as the optical reflectance NDVI and PRI vegetation indices, and a vertical profile sampling of air temperature and humidity, as well as wind speed and soil matric potential. We also considered understory solar interception sampling and/or up looking imagery. Many hand-held images were taken to investigate a view of the changing vegetative, mineral, and environmental measurement areas, and to image the biological target from different angles and positions. Therefore, what measurement approach would best compliment generic and collaborative phenotypic modeling? What total parameters are needed, and at what physical locations should measurements be taken? What temporal and spatial granularity and accuracy is desired?

Simulation modeling in silico of agricultural plants, to represent genetic, environmental, and managerial influence, and compute a predicted growth of the vegetative biological construct is now somewhat common. Yet to effectively create or validate the programmatic parameters employed, there is need for an accurate measurement of the environmental status within which the plants of known genetics are grown. Often a standard weather station is used to measure the micro-meteorological phenomena near genotypic accessions, where a course time granulation of basic environmental information is attributed as input to the simulation model. However, additional parameters at higher temporal frequency, that include soil and surface energetic status, and arrayed samples distributed in a vertical spatial profile, may add to an energy balance and thermodynamic spatial understanding, and perhaps further support compute simulation. The second in a series of three USDA in Maricopa Plant Group phenotyping team presentation notes, labeled “F119 2021 IRT stations mid-season report”, shows example data from a pair of modified weather stations recording soybean row-crop plants. The two stations each recorded 5Hz non-contact canopy surface area temperature emissions from a single row of plants, in both a nadir and angled view, along with local air temperature and humidity, incoming solar radiation, soil moisture and temperature at 20cm, and temperatures from several type-T thermocouple junctions placed in the soil at different depths. With a focus on thermal metrics, questions presented were: At what spatial locations does temperature sampling best support crop simulation modeling? For which soil depths, and how many total measurements are most valuable? Is a spatial profile of air temperatures or leaves within the canopy desired? How does the non-contact surface temperature recordings of infrared radiometers compliment the contact temperatures of thermocouple wire junctions? Can an angled radiometric thermometer better sample heat emission from a row of plants than a nadir view?

The simulation modeling in silico of agricultural plants represents genetic, environmental, and managerial influence, to compute the predicted growth of a vegetative biological construct. Yet to effectively create or validate the programmatic parameters employed, there is need for accurate measurement of environmental status within which plants of known genetics are grown. Often a standard weather station is used to measure micro-meteorological phenomena near the genotypic accessions, where a course time granulation of basic environmental information is attributed as input to the simulation modeling. However, additional parameters at higher frequency, that include soil and surface energetic status, and repeated samples distributed in a vertical spatial profile, may add to energy balance and thermodynamic understanding and perhaps further support compute simulation. The USDA in Maricopa Plant Group phenotyping team presentation note, “IRT stations initial setup”, the first in a series of three, shows initial setup notation of a modified weather station placed in the field of a 2021 soybean experiment conducted at the Maricopa Agricultural Center, USA. The station recorded 5Hz non-contact canopy area surface temperature of a single row of plants from both a nadir and angled view, along with air temperature and humidity, solar radiation, soil moisture, and temperatures from several type-T thermocouple junctions placed at the soil surface and on the underside of leaves. The question presented was, what metrics would best support crop simulation modeling and how best to sample them?

The sustainable production of upland cotton, an economically important fiber crop, is threatened by changing environmental factors including high temperatures and low-soil water content. Both high heat and low-soil water can reduce net photosynthesis resulting in low fiber yields or poor fiber quality. Leaf chlorophyll content has a direct relationship with photosynthetic rate. Understanding how high heat and low-soil water affect chlorophyll content can identify opportunities for breeding improvement that will lead to sustainable fiber yields. A two-year field trial located in Maricopa Arizona measured leaf chlorophyll content, available soil water, ambient air temperatures, and cotton growth measurements collected by a high-clearance tractor equipped with proximal sensors. The results showed that low-soil water significantly increased leaf chlorophyll content, while high temperatures significantly reduced content. Structured equation modeling revealed that cotton may divert available resources to leaf area and chlorophyll content for the production of photosynthates during periods of high temperatures.

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. This 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. https://data.nal.usda.gov/dataset/bronson-files-dataset-10-field-113-2018-cotton Visible in the cover image is Dr. Bronson operating his Hamby proximal sensing rig in the field.

Dr. Kevin Bronson provides a dataset representing the second of three consecutive years of cotton and nitrogen management experimentation in Field 113. 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. This dataset 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. https://data.nal.usda.gov/dataset/bronson-files-dataset-9-field-113-2017-cotton

Dr. Kevin Bronson provides a dataset representing the first of three consecutive years of cotton and nitrogen management experimentation in MAC farm Field 113, 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.

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. https://data.nal.usda.gov/dataset/bronson-files-dataset-7-field-13-2015

Dr. Kevin Bronson provides another nitrogen and water management in cotton agricultural research raw dataset for compute. 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.

Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs.

Dr. Kevin Bronson provides this unique nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes the complete raw sensors records and logger outputs. In the picture, Mr. Matt Hagler is shown starting the GeoScout device so to record AOS reflectance data from the PSCM1 platform 01/04/2013.

Data is on the National Agricultural Library’s, USDA Agricultural Data Commons, made available by Dr. Kevin Bronson, with intention to support machine read and compute awareness. In the dataset cover image, the Proximal Sensing Cart Mark1 is seen suspending a High-Throughput Plant Phenotyping sensing cluster in proximal environmental data acquisition operation. Views of first-year Guayule bush in flower are shown looking down crop rows, from the side, and in nadir. In the distance, older larger plants are visible. Parthenium argentatum is valued as a commodity plant today - https://www.bridgestone.com/technology_innovation/natural_rubber/guayule/ This “new crop” holds rich history and continued promise -https://doi.org/10.1016/j.indcrop.2015.03.042 Dr. Diaa Eldin M Elshikha is credited with recent and comprehensive study of this important rubber bush, integrating a wide depth and breadth of research disciplines and experts understanding this genetic asset. https://www.researchgate.net/publication/351244705_Nitrous_oxide_emissions_N_uptake_biomass_and_rubber_yield_in_N-fertilized_surface-irrigated_guayule https://www.researchgate.net/publication/332870724_High_guayule_rubber_production_with_subsurface_drip_irrigation_in_the_US_desert_Southwest https://www.researchgate.net/publication/353225626_Direct_seeded_and_transplanted_guayule_crop_coefficients_and_estimation_using_remotely_sensed_vegetation_indices https://www.researchgate.net/publication/343059008_Using_RGB-based_vegetation_indices_for_monitoring_guayule_biomass_moisture_content_and_rubber https://www.researchgate.net/publication/344754973_GENE_EXPRESSION_OF_GUAYULE_FIELD_PLANTS_UNDER_DROUGHT_STRESS_A_COMPARATIVE_RNA-SEQ_STUDY https://www.researchgate.net/publication/344754809_GUAYULE_GROWTH_AND_YIELD_OVER_TIME_AT_TWO_LOCATIONS_AT_HIGH_AND_LOW_IRRIGATION_TREATMENTS https://www.researchgate.net/publication/337367872_DORMANCY_AND_THE_GUAYULE_Parthenium_argentatum_G_SOIL_MICROBIOME https://www.researchgate.net/publication/337367637_ASSESSMENT_OF_IRRIGATION_REQUIREMENT_FOR_GUAYULE_USING_WINDS_MODEL https://www.researchgate.net/publication/316606450_A_comparative_life_cycle_assessment_of_flood_and_drip_irrigation_for_guayule_rubber_production_using_experimental_field_data https://www.researchgate.net/publication/353247094_Yield_responses_of_furrow-irrigated_guayule_using_deficit_irrigation

Data on the National Agricultural Library’s, USDA Agricultural Data Commons, made available by Dr. Kevin Bronson, with intention to support machine read and compute awareness. The dataset cover image, shows the University of Arizona’s Maricopa Agriculture Center field number 13, in USA, with cotton plants growing during Dr. Bronson’s 2013 summer experimentation season. In the lower-left corner of the image, an early PSCM1 platform is shown suspending our first sensor cluster configuration. In the lower-right, an ArcMap field interpolation of plant height examples processing done at the time. Above in the upper-right, two vantages looking down the crop row signify field geometry and condition as photo-documentations. Center in the image, behind crop rows of the young cotton plants, the USDA, ARS, Arid-Land Agricultural Research Center is visible in the distance. https://data.nal.usda.gov/dataset/bronson-files-dataset-2-field-17-2013

Data on the National Agricultural Library’s, USDA Agricultural Data Commons, recently made available by Dr. Kevin Bronson, with intention to support machine read and compute awareness. Nitrogen and water crop plant management experimental detail is reported, exampling also the methodology of the Proximal Sensing Cart platform and tri-metric sensing approach for high-throughput plant phenotyping in field conditions. https://data.nal.usda.gov/dataset/bronson-files-dataset-1-field-17-2012

Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.

Wolverine PSC carries our USDA Plant Group, Maricopa Phenotyping team, forefront proximal phenotyping methods applications, where acquisition of our phenotyping tri-metric of plant health status is combined with ambient environmental energetics sampling and imagery. Sensors are affixed to the rover platform either in a static location, or on a front rack with sliding adjustment on vertical rails. We are currently sampling two crop rows at one time, including a redundancy in both passive and active multi-spectral reflectance. And our newest methods include sampling air in a vertical array and low side views. Acquired together at 5Hz, all data is logged across four electronic devices linked by common GPS UTC. GPS stamped RGB video and images provide more sampling context and analysis input. Shown here in Field 119 of the Maricopa Agriculture Center Farm USA, Wolverine PSC prepares for its third run of Soybean season, sampling the experimental plots of Dr. Thompson and Dr. Herritt.

Plant height in field crops like cotton (Gossypium hirsutum L.) have long been of great interest to agronomists and plant breeders. Canopy height sensors can replace laborious point hand sampling and enable site‐specific management. Study objectives were to compare manual plant height measurements with ultrasonic distance sensor measurements in subsurface drip‐irrigated (SDI) cotton, and to assess the effects of nitrogen (N) and water management. Two Honeywell 943 sensors were used to weekly estimate canopy height in an N and water management study in Maricopa, AZ, USA from 2016 to 2018. Hand measurements of plant height‐ultrasonic‐sensed height correlation increased with growth stage with R2 of 0.92 to 0.99 at mid bloom. Root mean square errors (RMSE) ranged from 0.9 to 4.0 cm. Canopy height was strongly affected by N and water treatments, starting at first square. This rapid‐response ultrasonic distance sensor is accurate and has great potential as a canopy height measuring instrument in cotton. This article is protected by copyright. All rights reserved

Abstract Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.

Waking within the brave world of Maricopa Phenotyping, “best genetics” baby cotton plants emerge from drip irrigation tube embedded, fine loam raised soil beds. Marking the start of our summer season with staggered plantings, associated new proximal phenotyping hardware is set to deploy in our traditional trial Field #119 as part of Dr. Thompson’s larger body of research, including her pre-breeding selection evaluations. Here a visual-centric presentation is offered as a pictorial approach, in loosely documenting general experimental contextual elements about a month after planting, and perhaps allowing additional unique vantage based observations. I am looking to evaluate the merit of technically subjective based basic graphic presentation for purposes of phenotyping team conceptual development and quality control. So basically a quick image set and a report would be derived once a week, which could then be viewed by field-remote collaborators, and serve as a basic visual record. What do you see in the attached video slideshow? Your comments are welcome.

The micro-metrological weather sensing station has long been a standardized environmental reference collector in understanding localized energy dynamics. https://www.researchgate.net/publication/339945786_AzMET_Weather_Station_tribute_presentation Typically values are aggregated to a granularity of minutes. And yet we know that environmental fluctuation occurs quickly with significant effect as shown with the Eddy Covariance approach. However, it is important to be aware that there is also a primary atmospheric spatial observance easily obtained using an up-looking color camera. Exampled in the linked time-lapse video “WST_M1.mp4”, water vapor in the sky is apparent above a test setup mobile weather station as it records an array of sensors at 1Hz. In the past, I have successfully used imagery to support analysis of concurrent analog data, and moreover suggest the two are indeed compliments. For instance, by spatially separating radiometers and using a camera view of the sky, individual sensor difference could be explainable, when values are referenced to physical known model and a clear sky radiation signal. The mobile station example show in this video was presented previously wherein a GPS enabled video camera is listed as one of its exampled components. Here, the station was set on a gravel island at our location parking lot. At this location I made final mechanical observations and took a single position static comparison dataset. Part of that final dataset was analyzed by students of the ASU engineering undergraduate program as a class project (thank you Henry Shrock). https://www.researchgate.net/publication/336568914_ALARC_PlantGroup_Mobile_WST_2019_example

Humans resolve the physical environment largely through ocular construction; and a digital picture grid is a function of 2D table projections, in that an exposed color space value is eventually assigned to each spatially discrete pixel observed. Good and useful representations of target physical characters are subsequently created. Moreover, a simplified re-characterization and annotation of image pixel regions serve to segment or classify instances of similarity or quantification, supporting analysis. That is, understanding what is pictured. In the field phenotyping of plants, four basic image regions of character are present in nadir oriented medium field of view full solar illuminated proximal imagery: 1) Sunlit plant, 2) Shaded plant, 3) Sunlit soil, and 4) Shaded soil, here suggested that they are quadrilateral segments of phenotyping imagery. The attached presentation video examples taking a nadir view cotton phenotyping image taken from an iPad mini3 tablet camera, and where ImageJ software is used to demonstrate a basic image segmentation approach.

A tribute presentation in honor of the AzMet weather station in Maricopa Arizona, USA.

High-throughput electronic plant phenotyping in field conditions and within a proximal distance on terra, grows now beneath the more remote and common aerial collections supported by small cameras and advancing software and machine image technologies. In this context, current researchers may employ advantages of proximal measurement, such as in achieving higher resolution or increased physical interface tandem UAS. The attached 2020 Plant Group Phenotyping Team poster entitled, “Proximal Terrestrial Plant Phenotyping.pptx”, is presented as an overview of the 2019 technical program, core data acquisition status, where the Avenger rig, Professor and Wolverine Proximal Sensing Carts, and a mobile energy balance weather station are used along with imagery, to feed a developing proximal terrestrial phenotyping data method that supports multiple scientific objectives and involves multiple investigators.

Cotton flower images for example

In the phenotyping of field plants used as vegetative producer crops, UAS color or extended spectral imaging is effective, and recommended, for rapid canopy sensing and managerial decision support product input; and also to enable course level modeling. However more proximal imaging is recommended to detect the fine, weak, or transient signal biotic characters which are often lost in an amalgamation of information, such as when plants are viewed from a higher vantage. Moreover, consider the opportunity of in-field leaf transmittance imaging, herein presented as a program novel cotton leaf cell resolution level method.

A picture is worth a thousand words, and my intention here is to offer some example viewpoint pictures as food for thought around next phenotyping data captures.

Peltier thermoelectric cooling (TEC) technology was utilized to cool Nikon N1 cameras on Professor Proximal Sensing Cart (PSC), in F119 summer 2019 cotton, successfully extending the operation time of the cameras in the extreme heat encountered during mid-day plant phenotyping collections.

Threshing method note to support computer simulation modeling and validation.

The typical technical steps employed in operating the Avenger Rig field phenotyping platform in 2017 are listed.

List of 27 data processing jobs supporting the Avenger Rig field captures

A list of 69 topics for discussion, in order to fully understanding the scope of Avenger Rig field phenotyping service.

A general approach note to the measurement of soil moisture in an agricultural phenotyping field space.

Nitrogen fertilizer use efficiency (NUE) is low in surface-irrigated cotton (Gossypium hirsutum L.), especially when adding N to irrigation water. A NO3 soil-test algorithm was compared with canopy reflectance-based N management with surface- overhead sprinkler-irrigation in central Arizona. The surface irrigation studies also compared fertigation of N fertilizer with knifing-in of N and the addition of a urease and nitrification inhibitor (Agrotain Plus, Koch Agronomic Services, Wichita, KS) to urea ammonium nitrate (UAN). Cotton lint and seed yields responded positively to N fertilizer in all four site-years. Recovery efficiency (RE) of N at low N fertilizer rates (60 to 76 kg N ha–1) ranged from 21 to 61% with surface irrigation and from 81 to 97% with overhead sprinkler irrigation. Deep percolation below 1.8 m was 4 to 11% of applied surface irrigations and rain, but was undetectable in the overhead sprinkler. Leaching of NO3 was apparently the largest N loss pathway in the surface-irrigated system. Fertigating UAN into surface irrigation resulted in similar lint yields and RE as knifing UAN. Use of Agrotain Plus with UAN gave similar yields and RE as using UAN alone. Reflectance-based N management using normalized difference vegetation index-amber (NDVIA) saved 50% of N fertilizer of the full soil-test based dose without a yield reduction in three of four site- years. Nitrogen fertilizer was over-prescribed with the soil-test-based treatment. This may have been due to not accounting for N mineralization, which the reflectance method indirectly measures.

My target is an accurate volumetric soil moisture profile determination, parameterizing the crop available water, within and across a soil suspension medium. Sensing the soil moisture, spanning from a z=0 surface, to vertically below the root zone. The apparent water infiltration rate and the root penetration appearance are additional phenomena that help resolve a plant soil atmosphere continuum understanding.

Due to intrinsic variability in environmental systems, multiple and redundant measurements are recommended in order to capture accurate phenotyping growth system energetics information. A micro-meteorology type portable weather station suspending an array of sensors is useful in characterizing ambient environmental conditions for one or multiple places in time series. Attached herein, a modified Campbell Scientific type mobile weather station is shown temporarily positioned in the bed of a pickup truck as a test example setup. The data acquisition system is based on my model TRACE WST optimized DAQ, including system performance and power metrics linked to 1 Hz sensed primary data values.

Thermoelectric cooling is used in field phenotyping via application of the inefficient Peltier effect, where system exhaust heat moves to the environment and a deployed radiometric sensor device target is thermostatically controlled.

The Nikon N1 aw1 GPS action camera is set up for field phenotyping by locking exposure settings and view angle. In this video, Image exposure settings recommendations are offered for F-Stop 11 and incorporating a new physical solar interceptor intended to decrease image contrast.

Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.

Aspirated micro-environmental sensible heat sampling on a proximal phenotyping platform

Terrestrial high-throughput plant phenotyping in the field and subsequent first order data pre-processing for phenonomics

An easy-to-customize, low-cost, low disturbance, motorized, and adjustable proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions, motor specifications, and a remote operation application are given. The cart, named Professor, supports mounting multiple proximal sensors and cameras for characterizing plant traits grown under field conditions. Professor easily adapts to multiple sensor configurations supporting detection of multiple target traits and has two axes of adjustable clearance by design. Professor is useful as a field-based phenotyping platform and offers a framework for customized development and application.

This is supplemental material for, "A Flexible, Low-Cost Cart for Proximal Sensing" 2013 paper which describes the first iteration of our Proximal Sensing Cart Mark 1 build.

Field-based high-throughput phenotyping is an emerging approach to quantify difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts represent an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and specifically a deployment protocol, were developed to phenotype traits related to drought tolerance in the field. The cart-sensor package included an infrared thermometer, ultrasonic transducer, multi-spectral reflectance sensor, weather station, and RGB cameras. The cart deployment protocol was evaluated on 35 upland cotton (Gossypium hirsutum L.) entries grown in 2017 at Maricopa, AZ, United States. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87 hectare field averaged 2 h and 27 min and produced 50.7 MB and 45.7 GB of data from the sensors and RGB cameras, respectively. Canopy temperature, crop water stress index (CWSI), canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) differed among entries and showed an interaction with the water regime (p < 0.05). Broad-sense heritability (H2) estimates ranged from 0.097 to 0.574 across all phenotypes and collections. Canopy cover estimated from RGB images increased with counts of established plants (r = 0.747, p = 0.033). Based on the cart-derived phenotypes, three entries were found to have improved drought-adaptive traits compared to a local adapted cultivar. These results indicate that the deployment protocol developed for the cart and sensor package can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.