Pedro Andrade-Sanchez

Development and evaluation of a field-based high-throughput phenotyping platform

Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84ha h-1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2=0.86-0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2=0.28-0.90) and temperature (H2=0.01-0.90) traits. We also found a strong agreement (r2=0.35-0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.

Performance Assessment of Wireless Sensor Networks in Agricultural Settings

Wireless Sensor Network (WSN) utilizes radios operating primarily in the 900 MHz and 2.4 GHz frequency bands. In general, as frequency increases, bandwidth increases allowing for higher data rates but power requirements are also higher and transmission distance is considerably shorter. In general, depending on the operating environment, significant signal loss can occur at these frequencies particularly when the radios require line-of-sight for optimal performance, with 2.4 GHz more susceptible than 900 MHz. For agricultural applications, WSN must be able to operate in a range of environments, from bare fields to orchards, from flat to complex topography, and over a range of weather conditions, all of which affect radio performance. However, there are limited data on radio performance as affected by agricultural setting and no standard tests are available for quantifying WSN performance in agricultural applications. Using a low powered, 10 mW 900 MHz frequency hopping spread spectrum radio, we developed a range of tests intended to quantify the performance of agricultural WSN in fields, vineyards, and orchards over a range of crop and weather conditions. Performance data include different metrics of radio performance such as packet delivery and signal strength along with power consumption tests under different supply strategies. This paper evaluates the extent to which various tests can be used to quantify WSN performance and how WSN perform under various cropping systems.

Development, Construction, and Field Evaluation of a Soil Compaction Profile Sensor

Studies have shown that an increased level of soil compaction leads to a reduction in infiltration characteristics of soil, which in turn leads to low soil moisture. Conventional methods of measuring soil compaction are tedious, time consuming, and expensive. The objective of this study was to develop and evaluate a soil compaction profile sensor (SCPS) that could assist in the assessment of the state of compactness of the soil profile in real-time. The device developed in this study consisted of eight cutting elements, designed to provide information on soil resistance to cutting for every 7.5 cm layer down to a total depth of 60 cm. The design produced a sensor with a backward-sloping rake angle and a total thickness of 5.1 cm. Extensive field tests were conducted during summer and fall of 2001 and spring of 2002 in loamy, clayey, and sandy fields. Within each soil type, three different moisture conditions were included in the test (low, medium, and high). Analysis of the test data revealed that the soil cutting force was a function of soil bulk density, moisture content, and the location of the cutting element within the soil profile. Additional analyses were conducted to relate soil cutting force profile to the cone index profile. The empirical relationship between predicted and measured profile sensor output had a coefficient of multiple determination (R2) of 0.977, indicating that the SCPS can potentially be used to make real-time measurements of soil strength profile.

Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010–2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Field-based proximal hyperspectral data was collected for cotton phenotyping.3.68 billion PROSAIL runs on a supercomputer were used for model inversion.Partial least squares regression models best estimated four cotton phenotypes.High-throughput capability will improve hyperspectral methods for phenomics. Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content ( C w ), specific leaf mass ( C m ), leaf chlorophyll a + b content ( C ab ), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the physiological (or photochemical) reflectance index (PRI) were compared with partial least squares regression (PLSR) approaches to estimate the four phenotypes. Additionally, inversion of the PROSAIL plant canopy reflectance model was investigated to estimate phenotypes based on 3.68 billion PROSAIL simulations on a supercomputer. Phenotypic estimates from each approach were compared with field measurements, and hierarchical linear mixed modeling was used to identify differences in the estimates among the cultivars and water levels. The PLSR approach performed best and estimated C w , C m , C ab , and LAI with root mean squared errors (RMSEs) between measured and modeled values of 6.8%, 10.9%, 13.1%, and 18.5%, respectively. Using linear regression with the vegetation indices, no index estimated C w , C m , C ab , and LAI with RMSEs better than 9.6%, 16.9%, 14.2%, and 28.8%, respectively. PROSAIL model inversion could estimate C ab and LAI with RMSEs of about 16% and 29%, depending on the objective function. However, the RMSEs for C w and C m from PROSAIL model inversion were greater than 30%. Compared to PLSR, advantages to the physically-based PROSAIL model include its ability to simulate the canopys bidirectional reflectance distribution function (BRDF) and to estimate phenotypes from canopy spectral reflectance without a training data set. All proximal hyperspectral approaches were able to identify differences in phenotypic estimates among the cultivars and irrigation regimes tested during the field studies. Improvements to these proximal hyperspectral sensing approaches could be realized with a high-throughput phenotyping platform able to rapidly collect canopy spectral reflectance data from multiple view angles.

Development and Field Evaluation of a Field-Ready Soil Compaction Profile Sensor for Real-Time Applications

Although the first prototype of a soil compaction profile sensor (SCPS) developed at the University of California Davis performed well in terms of sensing variation in soil compaction level with operating depth, it indicated a need for design improvements in three aspects - cost, size, and operational characteristics. The goal of this study was to design, fabricate, and test a field-ready device with enhanced capabilities to sense the differences in soil compaction along a profile down to a depth of 46 cm. The new design had close resemblance to commercially available subsoiler shanks. This new sensor used five custom-made load cells with their rated capacity adjusted to their location along the shank to achieve consistent sensitivity. Cutting elements of 63.5 mm in height were directly connected to these force transducers. The total thickness of the sensor was 28.6 mm. This sensor was tested in a Yolo silt loam field at different moisture contents. The results indicated that the new sensor had similar response characteristics as the older prototype since its output correlated with soil moisture content and density just like its predecessor. Likewise, its output also correlated with soil cone index values very well. This sensor was interfaced with a Differential Global Position System (DGPS) to geo-reference its output. Field evaluation was performed at the farm level in two fields with soils typical of the Midwest United States. Numerous cone penetrometer readings were obtained to compare with the output of the soil compaction sensor. Results also indicated that the improved SCPS can detect differences in the compaction state of the soil profile reasonably well. Moreover, sensor data was processed to generate tillage depth prescription maps to show the feasibility of site specific tillage.

Evaluation of a capacitance-based soil moisture sensor for real-time applications

A low resonant frequency, dielectric-based soil moisture sensor developed by Retrokool, Inc. (Berkeley, Cal.) was slightly modified and tested under static laboratory conditions using soil from three different series (Capay silty clay, Yolo loam, and Metz Variant fine sandy loam) of contrasting textural composition. The sensor response consisting of frequency and amplitude measurements was recorded over a range of volumetric moisture contents and salinity levels. The results indicated that the sensor was insensitive to changes in soil texture. The modification to the sensing circuit improved the moisture detection range for the sensor. However, the sensor response was influenced by changes in soil salinity. Empirical analyses showed that a normalized sensor output was highly correlated with the soil conductance. Under laboratory conditions, these estimated conductance values correlated well with soil moisture content (r2 = 0.87). When this sensor was vehicle-mounted behind a tillage tool and tested under field conditions in a Yolo loam soil, estimated conductance values were well correlated with measured soil moisture content (r2 = 0.78). The results suggest the sensor has good potential for routine applications in real-time measurement of soil moisture for precision agriculture applications.

PERIODICITY AND STOCHASTIC HIERARCHICAL ORDERS OF SOIL CUTTING FORCE DATA DETECTED BY AN "AUTO-REGRESSIVE ERROR DISTRIBUTION FUNCTION" (AREF)

In this article, we describe a methodology to estimate typical soil properties such as moisture content and compactness using soil cutting force fluctuation information obtained by a conventional chisel. The data were obtained in a Yolo loam field under four different soil conditions: tilled dry (TD), tilled wet (TW), untilled dry (UD), and untilled wet (UW). In order to quantify the complexity of fluctuating patterns of soil cutting force and soil physical properties, we introduce a new time-series analysis technique, the auto-regressive error function (AREF). We found that the frequency distribution pattern of the modified time-series data with the time lag showed a very clear shift with change in the time lag. The AREF was developed to detect this pattern shift. The soil cutting force time-series data obtained using an instrumented chisel were analyzed using power spectrum and AREF techniques. The spatial power spectrum analysis detected periodicity under dry soil conditions. On the other hand, the AREF showed a very clear hierarchical order, which was caused by the existence of self-similarity in the fluctuating patterns of soil cutting forces under all four tested soil conditions. Two AREF parameters were found to be related to soil moisture content and cone index, but not bulk density.

Sensores de reflectância e fluorescência na avaliação de teores de nitrogênio, produção de biomassa e produtividade do algodoeiro

The objective of this work was to evaluate the potential of using reflectance and fluorescence sensors to assess the levels of N‑NO3 - in the petiole, plant biomass production and yield of cotton. A randomized complete block design was used in a 3x4 factorial arrangement, with four replicates. Treatments consisted of three cotton varieties (ST‑4288‑B2RF, ST‑4498‑B2RF and DP‑164‑B2RF) and of four N rates (0, 45, 90 and 135 kg ha -1 ). At 120 days after sowing, readings were done with optical sensors for canopy fluorescence and reflectance. There were no significant correlations for N‑NO 3 - in the petiole with the reflectance sensor indices; however, there was correlation to biomass production (0.39) and yield (0.32 to 0.41). The fluorescence sensor indices were significantly correlated to N‑NO 3 - in the petiole (0.34 to 0.61), biomass production (0.30 to 0.53) and yield (0.34). Compared to the reflectance indices, the fluorescence ones have a greater ability to assess the levels of N‑NO3 - in the petiole, a similar ability to detect variation of plant biomass, and a lower ability to detect

TERRA-REF Data Processing Infrastructure

The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Up to two TB of data per day are collected and transferred to the National Center for Supercomputing Applications at the University of Illinois (NCSA) where they are processed. In this paper we describe the technical architecture for the TERRA-REF data and computing pipeline. This modular and scalable pipeline provides a suite of components to convert raw imagery to standard formats, geospatially subset data, and identify biophysical and physiological plant features related to crop productivity, resource use, and stress tolerance. Derived data products are uploaded to the Clowder content management system and the BETYdb traits and yields database for querying, supporting research at an experimental plot level. All software is open source2 under a BSD 3-clause or similar license and the data products are open access (currently for evaluation with a full release in fall 2019). In addition, we provide computing environments in which users can explore data and develop new tools. The goal of this system is to enable scientists to evaluate and use data, create new algorithms, and advance the science of digital agriculture and crop improvement.