Biological Systems Engineering
Non Technical Summary
The overall goal of this project is to establish two complementary high intensity phenotyping sites at University of Nebraska-Lincoln (UNL) and Texas A&M University at College Station, TX (TAMU), focusing on maize and wheat. Our four specific objectives are: (1) Support two ongoing community-based plant phenotyping efforts at both UNL and TAMU; namely, the maize Genomes to Fields Initiative (G2F) and the winter wheat breeding programs; (2) Advance and apply Unmanned Aerial Vehicles (UAVs) as our main platform for sensor deployment and data collection, focusing on the development of new sensors and tools, as well as precise and consistent systems and protocols, to enable high throughput analysis of plant physiological and biochemical traits at leaf and canopy levels; (3) Disseminate phenotyping data broadly and promptly via public data repositories, and create image processing and analytical tools that provide end-to-end solutions to extract target traits from UAV images; (4) Train a next generation workforce in high throughput plant phenotyping through graduate and undergraduate research and by developing in-class curriculum, online educational material, and summer short-term training workshops. Over the life of the project, we anticipate making leaps in the science and engineering of plant phenotyping, including new sensor/tool development, data modeling and analytics, and workforce development. In the long term, we seek to replicate our gains by establishing a network of high throughput phenotyping sites nationwide with what we and others have learned.
Animal Health Component
Research Effort Categories
Goals / Objectives
The goal of this project is to forge a bi-regional high intensity phenotyping alliance focusing on maize and wheat and involving complementary sites at University of Nebraska-Lincoln (UNL) and Texas A&M University (TAMU). The project aims toaccelerate the development of accurate and repeatable phenotyping sensors and tools, data models and analytics, and the science and engineering workforce required for plant phenotyping to make the next leap. In the long term, we expect to use the knowledge we generate so that our gains can be replicated through a network of high throughput phenotyping sites nationwide.The specific objectives of this project are to:Enhance and further integrate two ongoing community-based plant phenotyping efforts at both UNL and TAMU; namely, the maize Genomes to Fields Initiative (G2F) and the winter wheat breeding programs;Advance the application of unmanned aerial systems (UASs) as our main platform for sensor deployment and data collection, focusing on the development of sensors and tools, as well as precise and consistent systems and protocols, to enable high throughput analysis of plant physiological and biochemical traits at leaf and canopy levels;Disseminate phenotyping data broadly and promptly via public data repositories, and create image processing and analytical tools that provide end-to-end solutions to extract target traits from UAS images;Train a next generation workforce in high throughput plant phenotyping through graduate and undergraduate research and by developing in-class curriculum, online educational material, undergraduate summer experiential learning, and high throughput plant phenotyping workshops.
Three locations in NE (Lincoln, North Platte, and Alliance) and three locations in TX (College Station, McGregor, and Bushland) are selected for high-intensity plant phenotyping activities. These locations capture a gradient of climate and soil conditions in both States, where genotypes can be robustly tested to elucidate how measured phenotypes are controlled by the complex genotype-by-environment interactions. In addition, a fourth location in NE (Mead) will be included to use UNL's Spidercam field phenotyping facility for high-precision, advanced phenotyping with a number of high-end imaging, ranging, and spectroscopic sensors.The project will grow and test ~250 maize hybrid lines from the maize G2F (Genomes to Fields) Initiative. This collection of lines will be replicated twice in each location, and the total number of maize plots will thus be ~1500 in each State. The project will also coordinate to grow and phenotype three wheat trials: (1) the F1 hybrid wheat trial; (2) the Southern Regional Performance Nursery; and (3) The Nebraska Interstate Nursery and the Texas Elite trial. The total number of wheat plots will be ~780 in each State.Environmental variables regarding the soil and micrometeorological data will be collected. A weather station will be installed at each experimental site (Watchdog or Arable Mark weather stations) to record total solar radiation, photosynthetically active radiation, maximum and minimum air temperature, wind speed, relative humidity, vapor pressure deficit, and precipitation amount. Each field site will also be installed with soil moisture sensors to measure volumetric soil moisture at two depths for the entire growing season. At the first year before planting, a soil apparent electrical conductivity survey and elevation survey will be conducted using an EM38 sensor connected to an RTK-GPS. Then a stratified sampling design will be employed to collect ~40 soil samples from each site. Soil samples will be analyzed for an array of physicochemical properties including particle sizes, organic matter, pH, nitrate-N, and extractable phosphorus and potassium.High-intensity plant phenotypic data collection will be planned to cover several levels of spatial and temporal resolutions. The first category will be the basic traits that plant breeders traditionally record at the key developmental stages. G2F specifies a list of such traits for maize that we will collect from the maize trials in this project. A standard list of wheat traits will be collected from the wheat trials, including fall plant emergence and ground coverage, height , winter survival, days-to-heading and days-to-anthesis, plant height at full extension, lodging, grain yield, grain protein content, moisture, test weights and single kernel characteristics (seed weight and grain hardness).The second category of phenotypes will be made by handheld sensors that can be deployed in the field for rapid, in vivo measurements. These traits include leaf chlorophyll content, leaf area index, stomatal conductance, and leaf hyperspectral reflectance (from which an array of vegetation indices can be derived). Many vegetation indices are correlated with important physiological and biochemical aspects of plants. Because these sensor-based measurements are fast and non-destructive, they can be taken at multiple time points across a growing season. To validate these sensor readings, leaf disks will be sampled from the selected plots and analyzed for a number of biochemical traits including chlorophyll content, leaf water content, nitrogen, phosphorus, and potassium.Large multi-rotor UAS equipped with a RGB, multispectral, and thermal infrared camera will be our main platform for UAS-based phenotypic data collection. Flight height will be 25 or 50 m. UAS imaging will be conducted on a weekly basis at Lincoln and College Station, and biweekly at other four locations. UAS imaging will also occur at key crop developmental stages, and coincide with the ground-level data collection. Raw UAS images will be mosaicked into field maps using Pix4D or AgiSoft Photoscan and further analysis will be conducted at plot level for structural and morphological features and vegetation index calculations.About 150 maize lines and 60 wheat hybrids will be selected to phenotype with the NU-Spidercam facility, occupying roughly 0.5 ac. of the field. This activity represents the phenotyping effort with the highest intensity in terms of spatial and temporal resolution, data throughput, and precision. The sensors used on NU-Spidercam include a RGB camera, a 4-band multispectral camera, a thermal IR camera, a hyperspectral camera, a VNIR spectrometer, and a 3D LiDAR. Imaging will take place on a weekly basis in the season. One unique aspect is that the sensor platform will measure each plot five to six times during a day to capture the diurnal variations of the key traits such as leaf temperature and leaf drooping, which respond rapidly to the environment variation.Three novel plant phenotyping data analysis and modeling tools will be developed. Firstly, hyperspectral imagery data from NU-Spidercam will be analyzed to extract narrow spectral bands that exhibit high correlations with canopy chlorophyll content, water content, N, P, and K at the plot level. After sufficient validations, these bands will be implemented on commercially available multispectral cameras (by reconfiguring their optical channels) to be used on UAS for other sites in the project. Secondly, a leaf- and canopy-level spectral library will be compiled and developed from the hyperspectral reflectance data of maize and wheat. This spectral library, when put into practice, will allow rapid, nondestructive, and low-cost estimation of chlorophyll, water content, N, P, and K in plants. One emphasis will be to explore advanced machine learning algorithms (including convolutional neural network and deep learning) to better model these biochemical traits from the hyperspectral data, as well as sufficiently validate these models across the six testing sites. Thirdly, a new approach to estimate the crop evapotranspiration (ET) trait at the plot scale by combining the thermal infrared and RGB imaging, co-measured micrometeorological variables, and a two-source energy balance model will be investigated. This new approach will be developed and validated with the phenotyping data from the NU-Spidercam system first, and then will be tested on the imagery data from the UAS at other testing sites.Education and training activities of this project will be focused on the following five aspects. First, a course on high throughput plant phenotyping technologies and applications will be developed and taught as UNL and TAMU. This course will target for senior undergraduate and graduate students in plant breeding, agronomy, and agricultural engineering. Second, a series of Open Education Resources in the form of online text lessons and videos will be developed on high-throughput plant phenotyping technologies and applications, with the goal of contributing to improved agriculture and science literacy. The materials will be housed on the Plant and Soil Sciences eLibrary (PASSeL) website of UNL and accessible to the general public. Thirdly, the project will develop and host Undergraduate Summer Learning Experience at UNL for a two-week immersion experience in research. Efforts will be made to recruit underrepresented groups by advertising with colleagues at Historically Black Colleges and Universities. Fourthly, the project will train four PhD students, one postdoc, and three professionals by directly participating in the research activities of this project. Finally, a two-day workshop focused on high-throughput plant phenotyping technologies (such as imaging processing and UAS) will be developed and held annually at UNL, targeting for 50 participants from both academic institutions and industry.