Source: UNIVERSITY OF NEBRASKA submitted to
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
Multistate No.
Program Code
Project Start Date
Sep 1, 2020
Project End Date
Aug 31, 2023
Grant Year
Project Director
Ge, Y.
Recipient Organization
Performing Department
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

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
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.
Project Methods
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.

Progress 09/01/20 to 08/31/21

Target Audience:The Nebraska teamworked with researchers from Purdue University, University of Minnesota, and Iowa State University and created a new Multistate Hatch Project NC1212: Exploring the Plant Phenome in Controlled and Field Environments. The teammade presentations (virtual zoom meeting)to Nebraska Congressional staff and USDA Farm Service Agency Nebraska leadership onhigh throughput plant phenotyping research at University of Nebraska-Lincoln and particularly highlighted this project. Ge gavetours to two US cabinet-level officialsfor theGreenhouse Innovation Centerat UNL and introduced this project to them. The project catalyzed research collaborations between UNL and Rothamsted Research at United Kingdom. Now two institutions have formal collaboration in crop phenotyping research using UAV and the field phenotyping systems (Spidercam at UNL and the LemnaTec field gantry system at Rothamsted). The team members presented the research and results fromthis projectin a number of professional society meetings. In both Texas and Nebraska, the research teamreached to corn and wheat producers, commodity boards (corn and wheat) andgeneral public. PI Yufeng Ge and Co-PI Yeyin Shi's groups attended University of Nebraska's East Campus Discovery Day on Jun/12/2021. This event was attended by thousands ofresidents in Lincoln. We introduced and demonstrated high throughput plant phenotyping technologies (such as remote sensing and UAVs), and explained how these technologies can help researchers to accelerateplant breeding cycles and improve food security in the future. Changes/Problems:Our project has a fewchanges in personnel. Firstly, co-PI Dr. StephenBaenziger retired from UNLin May 2021. His successor, Dr. Katherine Frels started at UNL in March 2021. As the new small grains breeder, she intends to fully utilize the advantage of high throughput phenotyping to modernize the UNL Small Grains (including wheat) Breeding program. As such, she is now actively participating in this grant and taking over Dr. Stephen Baenziger's roles and responsibilities. Secondly, co-PI Dr. Vikas Belmkar left UNLin Jun 2021. Vikas was responsible for Obj. 3 of our project. InSep 2021, Dr. Yu Pan was hired as a new data science specialist (at the rank of research assistant professor). Dr. Yu Pan is now participating in this grantand responsible for leading Obj. 3. Thirdly, co-PI Dr. Alex Thomasson left Texas A&M University and joined Mississippi State University. This change has minimum impact on the project. Dr. Alex Thomasson is still actively involved in the project and will continue to do so. His team is actively contributing to Obj. 2 of the project. A few aspects of the project were impacted by Covid-19. For most of the time in this reporting period, our universitieshadstrict policies for social distancing and travel restrictions. We were not allowed to share a vehicle when driving to the field sites, which hampered our progress in field work and data collection.Wereduced the speed of plant sample processing and analysis due to the need for social distancing among project personnel working in the lab. Travel restrictionssomewhat limited our capacity toattend professional society meetings and disseminate project results to the community. We were not able to implement the summer research experiential learningpart of Obj. 4 (which planned torecruitundergraduate students to come to UNL during the summer for research experiential learning program) because of the Covid.We hope as the trend with the Pandemic has been getting better, we will be able to return to normalcy and improve our productivity. We have started working with UNL's Office of Graduate Studies' Summer Research Program to plan for this activity in Summer 2022. What opportunities for training and professional development has the project provided?The entire team (including PIs, graduate students, and technical staff) have met virtually on a monthly basis. In each meeting,we had onestudent from alab to give a research seminar (45 minute long followed by a Q&A and discussion) on the topic of high throughput plant phenotyping. The topics covered so far included "UAV for wheat yield estimation", "autonomous ground control point design", "UAS data hub at Texas A&M University", and "seasonal UAV images and spectral index-based selection of corn". At UNL, three undergraduate researchers participatedin the project.The students were trained on how to use instruments (such as NDVI sensors, handheld leaf chlorophyll meters, and leaf area index meters) to measure plants in the field. They were also trained to use MATLAB and R for plant image processing and statistical analysis. One of the students was leveraging this research experience for formal internship experiential learning course credits. At UNL, Co-PI Yeyin Shi taught a 3-week mini course "Aerial Imagery Processing and Analysis Using Python" in Jan/2021.Fifteen students (both senior undergraduates and graduate students) from Biological Systems Engineering and Agronomy & Horticulture registered and attended this course. PI Ge gave a 2-hour webinar at Phenome Force (Nov-2020) and introduced hyperspectral technologies for leaf chemical and physiological trait analysis. This webinar was attended by ~100 researchers from all over the world. How have the results been disseminated to communities of interest?The project has resultedthree peer-reviewed journal articles and two referred conference proceedings. The team members presented the research and results fromthis projectin a number of professional meetings as listed below: Co-PI Seth Murray's group attended Corn Breeding Research Meeting (Virtual, Feb/20/2021), Soybean Breeder's Workshop (Virtual, 02/24/2021), andThe National Turfgrass Federation (NTF) and The Foundation for Food and Agriculture Research (FFAR) - Turf Stakeholder Summit II(Virtual,10/20-22/2020). Co-PI Alex Thomasson's group attendedNorth American Plant Phenotyping Network conference (Virtual, Feb/19/2021),Quad Cities ASABE meeting (Feb/23/2021),IEEE Stratus conference (May/17/2021), andInternational Symposium on Global Trends and Challenges of Smart Farming Technologies and Applications atKangwon National University, Korea (Jun/15/2021). PI Yufeng Ge's group attendedNorth American Plant Phenotyping Network conference (Virtual, Feb/20/2021) and SPIE Conference (Virtual, Apr/5/2021). Co-PI Yeyin Shi's group attended SPIE Conference (Virtual, Apr/5/2021) and the ASABE Annual International Meeting (Virtual, Jul/2021). What do you plan to do during the next reporting period to accomplish the goals?Obj. 1:We will continueto support the wheat breeding programs and the maize G2F initiatives in Nebraska and Texas. In Nebraska, the locations will be Lincoln, Mead, North Platte, and Scottsbluff (or Sidney). In Texas, the locations will be College Station,McGregor, Castroville, and Bushland. Leveraging these experiments, the team will continue to collect UAS images (with RGB andmultispectral cameras, and for some selected trials with thermal infraredand hyperspectral cameras) on a weekly or bi-weekly basis. Ground-truth measurements of key chemical and physiological traits will be collected, either using handheld sensors or by collecting leaf samples. The Spidercam field phenotyping system at UNL willbe used to collect intensive phenotyping data (with sub-day temporal resolution)from ~60 plots of corn and wheat each. Obj. 2: we will continue to develop new methods to extract digital traits from UAV images and related them to important chemical and physiological traits of wheat and corn. Specifically, we continue to investigate the use of hyperspectral data (both the hyperspectral imagery from UAV and leaf-level hyperspectral reflectance data) to estimate leaf/canopy traits including chlorophyll, nitrogen, and water content. This effort will allow us to identify useful spectral bands to quantify these traits and guide the design of low cost multispectral sensors (either handheld or cameras on UAV) to measure these important biochemical traits for corn and wheat breeding. We will continue to investigate the use of UAV thermal IR images combining the weather data for crop canopy evapotranspiration modeling. We will also investigate the time-series RGB and multispectral UAV images for yield prediction across multiple experimental sites to standardize the sensors and protocols for UAV flying and image acquisition. Obj.3: At Texas, the team will continue to leverage the UAS data hub to store, analyze, and disseminate the UAS image data collected at Texas. At Nebraska, we will continue to develop the integrated data management and analysis platform for high-throughput phenotyping data. Obj. 4: Regarding the online training material, we will complete the two modules on high throughput plant phenotyping of crop height and leaf area index, upload them to the Plant and Soil Science eLibrary, and test them thoroughly. We will begin to work on the third and four modules on crop evapotranspiration and hyperspectral reflectance. In the next reporting period, we expect to complete 50% of the latter two modules. Regarding the classroom teaching, Co-PI Yeyin Shi will continue to teach the course "Aerial Imagery Processing and Analysis Using Python" in spring 2022. One or two new class modules related to UAV thermal infrared and hyperspectral image processing (using data from this project) will be created and added. Another mini-course: "Computer Vision and Artificial Intelligence Applications in Plant Phenotyping", developed by Dr. Sruti Choudhury, will also be taught in spring 2022. The summer research experiential learning for undergraduates will be implemented Jun-Aug of 2022. A preliminary recruiting webpage has already been built with the project and hosting mentor information. In the next reporting period, we will focus on (1) improving the information webpage, (2) recruiting participants (with emphasis on minorities and HBCU), and (3) implementing the summer research experiential learning program at UNL.

What was accomplished under these goals? Obj. 1.For wheat in Nebraska, the focus of data collection was in the Lincoln and Mead sites and the main breeding trials screened by UAV were the hybrid trials and the F3:F6 preliminary yield trial (~1000 plots in total). UAV images were captured on a weekly basis from the beginning of April to end of June with RGB, multispectral, and thermal infrared cameras. At selected plots ground-truth measurements of plot height, stomatal conductance, and leaf area index (LAI) were also collected matching the UAV images. For wheat in Texas, the team led by co-PI Amir Ibrahim conductedUAV flights at four locations in Texas, namely College Station, McGregor, Castroville, and Bushland during the 2020 - 2021 wheat growing season. All flights were conducted with drones equipped with RGB and Multispectral cameras. The team tookground-truth data as well as agronomic and physiological data on chlorophyll content, canopy temperature, plant height,heading dates, and grain yield.Fourteen flights were conducted at College Station covering 723 plots. Nine flights were conducted at McGregor covering 1,340 plots. Three flights were conducted at the Multi-State Wheat Rust Evaluation Nursery at Castroville to capture response of plants to leaf rust, covering 60 plots and 14,000 observation rows.Twenty-five flights were conducted at Bushland covering 2000 plots per flight. G2F HIPS corn hybrid plots were planted both in Texas and Nebraska.In Texas, the team led by co-PI Seth Murray conducted UAV flights and image data collection throughout the growing season on a weekly basis. In Nebraska (the Lincoln and Mead sites), seven UAV flightswereconducted at each site starting early June till early September. Both RGB and multispectral images were acquired. At the time of tasseling, corn leaf samples (n = 1600) were collected from each genotype. The leaf samples were first subjected to ASD scanning to acquireVIS-NIR-SWIR hyperspectral readings and then processed to acquire chlorophyll concentration, leaf thickness, and leaf water content. A portion of the leaf samples (n=250) were sent to a commercial lab for chemical analysis including total nitrogen, phosphorus, and potassium. Another subset of the leaf samples (n= 96) were measured for photosynthesis and gas exchange traits using LICOR instruments. At UNL's Spidercam field phenotyping site, winter wheat plots and G2F HIPS corn hybrid plots were establishedand measured throughout their respective growing season. We maintained an average measurement frequency of twice each week from crop emergence to maturation. All imaging modules (including a hyperspectral camera) on the Spidercam were used for the measurement. Weekly ground-truth measurements were taken at the plot scale for plant height, leaf chlorophyll concentration, stomatal conductance, and leaf area index. Conventional phenotyping data such as flowering date and final yield/quality were also collected. Obj. 2: Using the data collected from Obj. 1, we have been developing both conventional and machine-learning models to predictleaf physiological and chemical traits of corn leavesfrom the ASD hyperspectral reflectance data. The conventional method included the calculation of vegetation indices from the hyperspectral data, and the machine-learning methods included Partial Least Squares Regression, Random Forest, Support Vector Regression, and Neural Networks. Our preliminary results showthat several leaf traits (chlorophyll, nitrogen, leaf thickness, etc.) can be predicted from hyperspectral data satisfactorily. Weinvestigated how the plot-level traits such as LAI and crop water use (evapotranspiration, or ET) can be modeled from the UAV multispectral and thermal infrared images. For LAI, we found that usingboth the coverage of green pixels and plot height can predict plot-level LAI quite accurately. Including the canopy spectral information (such as red-edge NDVI) and canopy temperature can further improve LAI modeling. For ET, we found that (1) accurate segmentation of plot-level thermal IR images to separate the vegetationtemperature from the soil temperature can potentially improve ET estimation; (2) the combination of instantaneous weather data (solar radiation, air temperature, vapor pressure deficit, wind speed) and the UAV multispectral/thermal IR images allows the use of a two-source surface energy balance model to estimate instantaneous ET from the crop; and (3) flying UAV multiple times during a day enable theestimation ofinstantaneous ETmultiple times during a day, therefore provides an opportunity to improve the daily and seasonal estimation ET from remote sensing. Co-PI Thomasson's group has designed a new air-ground cooperative robotic system for UAV image calibration. This automated system has the potential to greatly reduce the amount of work needed for the geometric and radiometric calibration of multispectral, hyperspectral, and thermal IR images from UAV platforms. Obj. 3. In Texas, the UAV images from both wheat and maize (G2F) programs aretransferred to and stored at UAS Data Hub (Texas A&M University at Corpus Christi). In Nebraska, the wheat teamhas beenworking on developing a pipeline for analyzing and utilizing the UAV imagedata in the breeding program. In addition, the new data scientist (Dr. Yu Pan) is designing and developing a new agricultural data management and analysis platform. All the high throughput plant phenotyping data generated from this project will be curated in this new platform for more efficient analysis, modeling and dissemination. Obj. 4. Co-PI Leah Sandall and graduate student Catherine Mick are working to developonline educational materials for high-throughput plant phenotyping. We have finalized the plan to develop four modules for morphological and physiological traits of plants from the UAV platforms. These four modules are plant height, LAI, canopy evapotranspiration, and leaf/canopy hyperspectral reflectance data. The first two modules (plant height and LAI) are now under development. Each module contains a webpage (with texts and illustrations), a 20-minute video, and quiz-type questions for evaluation. After completion, these modules will be placed in UNL's Plant and Soil Science eLibrary for dissemination. Co-PI Yeyin Shi taught a 3-week mini course "Aerial Imagery Processing and Analysis Using Python" in Jan/2021. The course focused on the collection, processing, and crop trait extraction of UAV images, as well as python programming. Fifteen students (both senior undergraduates and graduate students) from Biological Systems Engineering and Agronomy & Horticulture registered and attended this course. This course fills an emerging need at UNL for courses in data-driven plant science, phenotyping,and digital agriculture. We have worked with UNL's Office of Graduate Studies to plan for theresearch experiential learningprogram in summer 2022. We have identified four researchers from our project team who are willing to host undergraduate researchers. We are developing a website to introduce the HIPS project at UNL in general, as well as the research focus of each participating lab. This website will be used for recruiting in the next reporting period.


  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Adak, A., Murray, S. C., Anderson, S. L., Popescu, S. C., Malambo, L., Romay, M. C., & de Leon, N. (2021). Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize. The Plant Genome, e20102.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Adak, A.; Murray, S.C.; Bo~inovi?, S.; Lindsey, R.; Nakasagga, S.; Chatterjee, S.; Anderson, S.L., II; Wilde, S. Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression. Remote Sens. 2021, 13, 2141.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Han, X., J. A. Thomasson, V. Swaminathan, T. Wang, J. Siegfried, R. Raman, N. Rajan, and H. Neely. 2020. Field-based calibration of unmanned aerial vehicle thermal infrared imagery with temperature-controlled references. Sensors 20:7098; doi: 10.3390/s20247098.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Wang, L., Jiating Li, Lin Zhao, Biquan Zhao, Geng Bai, Yufeng Ge, and Yeyin Shi. 2021. Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation. Proceedings Volume 11747, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI; 1174703.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zhao, L., Lin Wang, Jiating Li, Geng Bai, Yeyin Shi, and Yufeng Ge. 2021. Toward accurate estimating of crop leaf stomatal conductance combining thermal IR imaging, weather variables, and machine learning. Proceedings Volume 11747, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI; 117470L.