Source: UNIVERSITY OF NEBRASKA submitted to NRP
ADVANCING PLANT PHENOTYPING FROM GREENHOUSE TO FIELD PLOTS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1011130
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Oct 1, 2016
Project End Date
Sep 30, 2021
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
UNIVERSITY OF NEBRASKA
(N/A)
LINCOLN,NE 68583
Performing Department
School of Natural Resources
Non Technical Summary
Recent technological advances in high throughput plant phenotyping have made it possible to collect large amount of images and non-invasive sensor data on the performance of individual plants and plots at multiple scales to address the genotype by environment interactions. However, validity and transferability of results across scales remain a challenge. Additionally, software methods for converting raw images into information that are useful for evaluating plant performance for desirable traits, as well as methodologies for modeling how the genetic background of plants interact with variation in the environment, (i.e. temperature, rainfall, soil quality, etc.) are not yet well developed. In this project, we propose to develop and evaluate new methodologies that will ultimately allow plant scientists to evaluate and select individual plants for their valuable traits for specific environment and objectives. An important outcome of this project will be the development of technological and computational resources to accelerate the development of linkages between genotype and phenotype for stress tolerance traits in major crops. An autonomous platform that carries multiple sensor modalities to collect high resolution plant phenotypic data in the field will be developed.
Animal Health Component
25%
Research Effort Categories
Basic
50%
Applied
25%
Developmental
25%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2031549102050%
4025310202025%
1020430209025%
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 402 - Engineering Systems and Equipment;

Subject Of Investigation
1549 - Wheat, general/other; 0430 - Climate; 5310 - Machinery and equipment;

Field Of Science
2090 - Statistics, econometrics, and biometrics; 2020 - Engineering; 1020 - Physiology;
Goals / Objectives
Development of an innovative, integrative systems approach is imperative to addressing current and future challenges related to global food security. Improvements in agricultural management practices, technological advancements, and breeding programs are keys to addressing these challenges considering climate uncertainties and change. High Throughput Phenotyping Platforms (HTPP) - e.g., the LemnaTec Scanalyzer system, offer the opportunity to integrate proximal remote-sensing and imaging methods, together with abiotic/biotic environmental data acquisition, and selected plant responses, to better understand the genotype x environment (GxE) interactions.The goal of this project is to advance high throughput plant phenotyping solutions, and test techniques along a spatial and temporal continuum of plant growth conditions ranging from the controlled greenhouse environment to field plots, to better understand the genotype by environment interactions.Objective 1 Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field.Proposed work: test the morphological and biophysical traits of wheat genotypes using HTPP, and the sensitivity of this method in detecting genotypic differences under favorable conditions and under water stress, in the greenhouse and under field conditions, and validate results using traditional leaf to plant levels (Awada, Walia, Clemente, Biagorria and Ge).Expected outcome: We will characterize a mapping population of wheat genotypes with diverse drought response under controlled and field environments under well-irrigated and drought stressed conditions. Because of existing genotypic (SNP) information for this population, we will aim to map the genes involved in drought tolerant response in wheat. Outcomes will also include identification of wheat genotypes which exhibit tolerance under both experimental set-ups. We expect to discover novel phenotypes that have the potential for translating well between greenhouse and field studies. Such sensor-based phenotypic markers will be valuable for improving breeding for drought tolerance in wheat and potentially other cereals such as maize.Objective 2 Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems.Proposed work: Use existing and develop new high-throughput sensing and robotic techniques to measure plant traits and microenvironment parameters at single plant resolution for the purpose of comparing single plant to greenhouse and plot (field) level techniques (Awada, Ge, Walia).Expected outcome: Establish an affordable single plant sensor network to improve characterization of plant soil and aerial microenvironments in the fields at single plant resolution. By the end of the 5 year project, we anticipate to have a phenotyping robotic system that carries multiple sensing modules and is fully automated to collect multi-modal plant phenotypic data at single plant resolution. The robot will be readily used by plant breeders, agronomists and physiologists to assess morphological and biophysical traits of field grown plants.Objective 3 Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques.Proposed work: Develop a technique to translate phenotypic measurements in the greenhouse and field into crop model genetic coefficients, and use dynamic crop models to estimate the potential performance and yield of the phenotyped plants and scalability of across multiple spatial scales (Baigorria).Expected outcome: development of models to predict and forecast performance of crop genotypes in designated regions under variable soil and microclimatic conditions.
Project Methods
Objective 1 Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field.Greenhouse study: A greenhouse study will be conducted using the LemnaTec Automated Greenhouse High Throughput Imaging and Sensing system, at the Nebraska Innovation Campus Phenotyping Facility at UNL. Select wheat genotypes will be grown in 7-8 L pots, with standard greenhouse mix, and specified water treatments, photoperiod, day:night temperatures and relative humidity (treatments will depend on the genotype we use). We focus on wheat and drought stress since it is one of the major factors limiting productivity in the USA and elsewhere. Based on previous work, we have identified several wheat genotypes with diverse root architectural traits that can potentially influence water uptake and hence drought response. We plan to utilize these genotypes for drought studies in the greenhouse system. Further, through collaboration with colleagues working on durum wheat, we have access to mapping population between wild and domesticated durum wheat. Individuals within this population are known to differ in drought tolerance (unpublished work). We will also characterize this population to generate image-based phenotypic data and link it to genotypic information for this population. This component will bolster the development of approaches for linkaging genotype-to-phenotype for wheat and potentially may other species.Imaging and Sensing will be performed with 4 cameras. These include the LemnaTec Scanalyzer 3-D VIS (380-780 nm) for investigating morphological and growth parameters; LemnaTec Scanalyzer 3 D FLUO Fluorescence (excitation between 400-500 nm) for chlorophyll fluorescence and PSII activity (this activity is impacted by developmental stage and stresses); LemnaTec Scanalyzer 3 D IR (Thermal 900-1700nm) for water relations and abiotic stresses sensing; and Headwall Hyperspec Inspector 3 D (550-1700 nm; individual bands data acquisition capabilities) for abiotic and biotic stresses, absorption and reflectance of pigments (e.g., chlorophyll and carotenoids), relative determination of the concentration of these pigments, and photoprotective responses to stress. Data will be processed using the Lemnatec, and open source image (UNL and other institutions) processing softwareNon-destructive concurrent measurements will be conducted to assess the validity of techniques. These including gas exchange, leaf level chlorophyll content, and leaf level spectral characteristics (Ocean optics spectrometer, FL). Destructive complementary or ground truthing measurements will be conducted throughout the study. Destructive measurements will include leaf/stem area and weight, height, plant water potential, relative water content, biomass, specific leaf area, carbon isotope ratio, etc.Field Study: Wheat genotypes investigated under greenhouse condition using HTPP techniques will be examined under field conditions, and transferability and persistence of results for select traits across gemotypes and scales will be determined. When the UNL field phenotyping Spidercam© system (Austria) becomes available at the Agricultural Research and Development Center (ARDC) NE, in 2017, a diversity panel of wheat genotypes (same as greenhouse genotypes) will be examined. Plot scale (resolution for imaging ranges from few cm to 6 or 12 m2) imaging with the Spidercam© platform will be conducted with visible (RGB), IR, LIDAR, and multispectral capabilities. Automated plant phenotyping data will be collected at each site in each year throughout development and compared to leaf and plant level non-destructive measurements as described above, as well as, yield. Additionally, twenty randomly distributed individual plants will be marked per genotype per plot. These plants will serve as a basis of comparison for estimates of growth rate obtained from the field phenotyping system compared to single plant phenotyping data. Single plant phenotyping data will be used to estimate the contributions of G, E, and G×E to the phenotypes (Objective 2). For traits with large genetic effects, genome-wide-association mapping will be used to identify putative causal loci. For traits which show large year-to-year variation, indicating genotype by macro-environment interactions, or large field position effects, indicating GxE interactions, GWAS would instead be conducted using synthetic traits which estimate the size of the GxE effect in each line.Objective 2 Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems.The focus in this objective is on the development of a light-weight and flexible sensor platform that will be operated manually in the test plots (see Objective 1, field study) for single plant phenotyping which will complement the plot phenotyping efforts at ARDC. The platform will be able to measure four rows of plants simultaneously at a row spacing of 30". The sensor suite initially mounted will include ultrasonic height sensors, canopy NDVI sensors, and canopy temperature sensors. We anticipate the deployment of a prototype in year two of the project. Then after, we will expand the platform (years 3-5) to include portable spectrometers for plant canopy reflectance measurement, scanning LIDAR for plant structure, and multispectral cameras for plant imaging.We will establish a sensor network to characterize soil and aerial microenvironments in our study plots at single plant resolution. Each sensing node will include an above ground and an underground section. The underground section will consist of soil moisture, temperature, and EC sensors (at three different layers in the root zone; 0-20, 20-40, and 40-60 cm) to measure dynamics of these soil properties. The above ground section will include quantum, temperature, wind and relative humidity sensors to characterize dynamics of micro-environments. The sensor network, when operational, would produce large volumes of three dimensional sensing data (two dimensions in space and one dimension in time) that will be transferred wirelessly to characterize the dynamics of micro-environments close to individual plants. We will compare these field soil and plant aerial microclimate data to the greenhouse data, and identify the patterns or ranking of measured plant phenotypes under similar field and greenhouse environment.Objective 3 Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques.Information on traits from image analysis in the greenhouse and field, will be used and translated into the genetic coefficients needed to run the Decision Support Systems for Agrothecnology Transfer (DSSAT ) WHEAT-CERES crop model. Each derived parameter will be used to calibrate the model under the set of environmental conditions occurring during experiments. After developing the methodology to efficiently translate phenotyping information into genetic coefficients, the newly parameterized lines will be used to model crop performance (e.g., yield) as impacted by seasonal climate variability at regional and national scales using CropClimate© platform. Under the CropClimate platform, each set of genetic coefficients that represent a given wheat cultivar, will be virtually planted under all the climate/soil conditions available in CropClimate platform. After finishing this process, yield and performance maps for each phenotyped genotypes will be generated. This process will allow for selecting specific genotype for specific environment under specific crop management.

Progress 10/01/16 to 09/30/21

Outputs
Target Audience:The target audience included peers, professionals, graduate and undergraduate students, and private and public sectors in the areas of areas of plant breeding, agronomy, plant physiology, agricultural and biological engineering, computer vision, artificial intelligence, and sensors development. Team memberspresented their research findings at professional meetings and engaged with peers in other universities through invited talks nationally and internationally. UNL and our team are active in theNorth American Plant Phenotyping Network. Awada gave a Zoom seminar to the American Universtiy of Beirut, Lebanon and to the Smart Farm initiative, Portugal. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project provided support to several professionals, training to undergraduate and graduate students, and professional development to post-doctoral fellows and visiting scientists. Our team also repond to inquiries about facilities development and has been engaged with development of online YouTube trainining. How have the results been disseminated to communities of interest?Principle investigators, students and posdocs presented findings at professional meetings. Pis were invited speakers and have incoportated findings in classroom materials, and offered tours of the facilities. PIs have incorporated findings in classroom materials. Co-PI Shi has delivered two new graduate and undergraduate level classes related with the HTPP research results - AGEN/BSEN/MSYM-492/892 Aerial Imagery Processing and Analysis Using Python and MSYM-892 Technologies and Techniques for Digital Agriculture. Das Choudhury a research faculty developed a classe on methods in plant phenomics. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The goal of this project is to advance high throughput plant phenotyping solutions, and test techniques along a spatial and temporal continuum of plant growth conditions ranging from the controlled greenhouse environment to field plots, to better understand the genotype by environment interactions. Objective 1 Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field. We continue to contribute to the development of novel advanced methods and tools to characterise plants morphological, physiological and biophysical traits, using machine learning and artificiel intelligence, and validate them against handheld devises and destrictive and non destructive measurements. We have published several method manuscripts on automated leaf tracking, 3D reconstruction, stress detections, and scaling. We have worked with several crops including corn, sorghum, tobacco, wheat, rice, and camelina. (see puplication list) Objective 2 Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems. Since 2019, we have collected Spidercam, UAV-based multispectral and thermal imaging data on multiple crops and agroecosystems in NE. Results showed that it is possible although challenging to evaluate genetic by environment by management interactions within a field-based breeding context. Environment factors compounded with the hundreds of genotypes each with a different physiological response to stres made the field-based sensing challenging. However, multiple types of sensing data, rather than a single type, contribute to the evaluation and prediction. Also, selecting appropriate growth stages and timing in a day could increase the chance of success. (see publication list) Objective 3 Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques. We have been collecting UAV-based phenotypic data for UNL researchers. This is a rich dataset mainly including RGB and multispectral imaging throughout the growing season. The spatial resolution varied from sub-centimeter to a few centimeters for different trials and applications, so it is possible to do single plant level analysis though most of the analysis so far is plot level to be consistent with the ground truth and interests for the small grains program.We have been conducting leaf area index (LAI) estimation using UAV derived multimodal data for a couple of seasons. LAI is an index closely related with plant canopy cover, biomass, growth vigor and stress level. It is also one of the few important variables used in the calibration process of several well-recognized crop models. Despite of its importance, studies on estimating LAI in a high throughput manner in field from remotely sense data have not been widely evaluated on breeding settings. In another words, study performance varied among applications from single or a few varieties to many different varieties. And the performance was especially unstable for the performance on many different varieties which is common for a breeding program. Hence, our objective here is to systematically evaluate existing methods for LAI estimation and develop state-of-the-art method based on drone based remote sensing to improve LAI estimation within a breeding context. To achieve this, we have conducted field based experiments for two years in Lincoln, Mead, and Clay Center in NE. We evaluated standard destructive scanning and handheld LAI sensing, as well as drone and satellite based remote sensing methods. For the drone based methods, we investigate the performance of single sensor and the performance from the combination of multiple sensors. Classical statistical modeling and machine learning models are also evaluated to see their contributions on improving the LAI estimation accuracy. Promising results have been achieved and we found a significant improvement on estimation accuracy on the estimated LAIs for a large group of genotype when multimodal sensing data and machine learning models are applied. Publications are under preparation. Pis or Co-Pis were successful in received grants from NIFA. Our team co-led the developmentof a new multistate hatch proposal that started on October 1, 2021. We will continue this work moving forward in collaboration with partners from across the North Central Region and beyond.

Publications

  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Harel Bacher, Feiyu Zhu, Tian Gao, Kan Liu, Balpreet K Dhatt, Tala Awada, Chi Zhang, Assaf Distelfeld, Hongfeng Yu, Zvi Peleg, Harkamal Walia. 2021. Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress. Plant physiology 187 (3), 1149-1162
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Srinidhi Bashyam, Sruti Das Choudhury, Ashok Samal, Tala Awada. 2021. Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis. Remote Sensing 13 (5), 961
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Tala Awada. 2021. Role of High Throughput Plant Phenotyping in Addressing Current and Emerging Issues in Agricultural Research. Invited Talk, Dec 7, 2021. American University of Beirut.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: de Castro, A. I., Shi, Y., Maja, J. M., & Pe�a, J. M. (2021). UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions. Remote Sensing, 13(11), 2139.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Alzadjali, A., A. N., Alali, Veeranampalayam-Sivakumar, M. H., Deogun, J. S., Scott, S., Schnable, J. C., & Shi, Y.* (2021). Maize Tassel Detection from UAV Imagery Using Deep Learning. Frontiers in Robotics and AI, 8, 136
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2021 Citation: Sankaran, S., Marzougui, A., Hurst, J.P., Zhang, C., Schnable, J.C., Shi, Y. (2021). Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions? Transactions of the ASABE. (in press). (DOI: 10.13031/trans.14197)
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Zhao, B., Li, J., Baenziger, P.S., Belamkar, V., Ge, Y., Zhang, J., & Shi, Y.* (2020). Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management. Agronomy, 10, 1762. (DOI: 10.3390/agronomy10111762)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Li, J., Wijewardane, N., Ge, Y., Shi, Y. (2021, July 12-16). Retrieve Leaf Nitrogen and Water Status from VIS-NIR-SWIR spectroscopy using a Hybrid Inversion Method [Conference presentation]. ASABE 2021 Annual International Meeting, Virtual.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zhao, B., Khound, R., Santra, D., Shi, Y. (2021, July 12-16). Applying Computer Vision to Detect Heads of Proso Millet from UAV Images. [Conference presentation]. ASABE 2021 Annual International Meeting, Virtual.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Wang, L., Li, J., Zhao, L., Zhao, B., Bai, G., Ge, Y., & Shi, Y. (2021, July 12-16). Evaluating leaf area index estimation performance with few important features from UAS imagery and machine learning algorithms. [Conference presentation]. 2021 ASABE Annual International Virtual Meeting.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Wang, L., Li, J., Zhao, L., Zhao, B., Bai, G., Ge, Y., & Shi, Y. (2021, April 11-15). Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI (Vol. 11747, p. 1174703). International Society for Optics and Photonics.


Progress 10/01/19 to 09/30/20

Outputs
Target Audience:The target audience included peers, professionals, graduate and undergraduate students, and private and public sectors in the areas of areas of plant breeding, agronomy, plant physiology, agricultural and biological engineering, computer vision, artificial intelligence, and sensors development. Team members, postdoctoral fellows and graduate students presented their research findings at professional meetings and engaged with peers in other universities through invited talks nationally and internationally. We also reached out to the broader national and international community through Youtube videos. Ge gave an invited online seminar to introduce hyperspectral analysis for plant phenotyping (Leaf-level hyperspectral reflectance to rapidly estimate plant chemical traits - YouTube), as part of the community developed Phenome Force series sponsored by North American Plant Phenotyping Network. Awada gave a seminar to aHistorically Black College PVAMU, where shepresented a seminar on plant phenomics to their agricultural community. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project provided support to several professionals, training to undergraduate and graduate students, and professional development to post-doctoral fellows and visiting scientists. Our team also repond to inquiries about facilities development and has been engaged with development of online YouTube trainining. How have the results been disseminated to communities of interest?Principle investigators, students and posdocs presented findings at professional meetings. Pis were invited speakers at several conferences and workshops in the US and elsewhere, and have incoportated findings in classroom materials, and offered tours of the facilities. Pis also gave seminars. PIs have incorporated findings in classroom materials. Co-PI Shi has developed two new graduate and undergraduate level classes related with the HTPP research results - AGEN/BSEN/MSYM-492/892 Aerial Imagery Processing and Analysis Using Python and MSYM-892 Technologies and Techniques for Digital Agriculture. Das Choudhury a research faculty developed a classe on methods in plant phenomics. What do you plan to do during the next reporting period to accomplish the goals?1. Continue to make progress on the three objectives 2. Submit additional grant proposals to secure funding for this project 3. Mentor undergraduate and graduate students 4. Provide professional opportunities to postdocs and professionals 5. Disseminate findings

Impacts
What was accomplished under these goals? The goal of this project is to advance high throughput plant phenotyping solutions, and test techniques along a spatial and temporal continuum of plant growth conditions ranging from the controlled greenhouse environment to field plots, to better understand the genotype by environment interactions. Objective 1Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field. We continue to contribute to the development of novel advancedmethods and tools to characterise plants morphological, physiological and biophysical traits, using machine learning and artificiel intelligence, and validate themagainst handheld devisesand destrictive and non destructive measurements. We have published several methodmanuscripts on automated leaf tracking, 3D reconstruction, stress detections, and scaling. A book led by a research assistant professor on the team was published on novel methods for plant phenomics (Intelligent Image Analysis for Plant Phenotyping, A. Samal and S. Das Choudhury, 2020).We have worked with several crops including corn, sorghum, tobacco, wheat, rice, and camelina. Objective 2Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems. In 2019 and 2020, we collected two seasons of UAV-based multispectral and thermal imaging data on bioenergy sorghum drought tolerance trials in west Nebraska (Scottsbluff, NE). Results showed that it is possible although challenging to evaluate drought tolerance with UAV-based remote sensing imaging within a field-based breeding context. Environment factors compounded with the hundreds of genotypes each with a different physiological response to drought made the field-based sensing challenging. However, multiple types of sensing data, rather than a single type, contribute to the evaluation and prediction. Also, selecting appropriate growth stages and timing in a day could increase the chance of success. We are working on a journal publication based on this work. Several other manuscripts were published. Objective 3Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques. We have been collecting UAV-based phenotypic data for UNL researchers. This is a rich dataset mainly including RGB and multispectral imaging throughout the growing season. The spatial resolution varied from sub-centimeter to a few centimeters for different trials and applications, so it is possible to do single plant level analysis though most of the analysis so far is plot level to be consistent with the ground truth and interests for the small grains program. On top of the in-season growth description and grain yield prediction explored earlier, we mainly focused on leaf area index (LAI) estimation and lodging detection in 2020. The LAI has been estimated using various information extracted from the remotely sensed imagery using machine learning models. The estimated LAI together with other management and observation information have been used as key parameters in the yield prediction using a process-based crop model (APSIM). We have recently made significant progresses on calibration the model for one of the check variety. PIs also were Pis or Co-Pis on successful grants from NIFA.

Publications

  • Type: Journal Articles Status: Awaiting Publication Year Published: 2021 Citation: Sankaran, S., Marzougui, A., Hurst, J.P., Zhang, C., Schnable, J.C., Shi, Y. * (2021). Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions? Transactions of the ASABE. (in press). (DOI: 10.13031/trans.14197)
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Zhao, B., Li, J., Baenziger, P.S., Belamkar, V., Ge, Y., Zhang, J., & Shi, Y.* (2020). Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management. Agronomy, 10, 1762. (DOI: 10.3390/agronomy10111762)
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Pang, Y., Shi, Y., Gao, S., Jiang, F., Veeranampalayam-Sivakumar, A.N., Thompson, L., Luck, J., & Liu, C. (2020). Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery. Computers and Electronics in Agriculture, 178, 105766. (DOI: https://doi.org/10.1016/j.compag.2020.105766)
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Deng, X., Thomasson, J. A., Pugh, N. A., Chen, J., Rooney, W. L., Brewer, M. J., & Shi, Y.* (2020). Estimating the severity of sugarcane aphids infestation on sorghum with machine vision. International Journal of Precision Agricultural Aviation, 3(2). (DOI: 10.33440/j.ijpaa.20200302.89)
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Veeranampalayam Sivakumar, A. N. V., Li, J., Scott, S., Psota, E., J Jhala, A., Luck, J. D., & Shi, Y.* (2020). Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid-to Late-Season Weed Detection in UAV Imagery. Remote Sensing, 12(13), 2136. (DOI: 10.3390/rs12132136)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Al-Zadjali, A., Shi, Y.*, Scott, S., Deogun, J. S., & Schnable, J. (2020, April). Faster-R-CNN based deep learning for locating corn tassels in UAV imagery. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (Vol. 11414, p. 1141406). International Society for Optics and Photonics.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Zhao, B., Li, J., Wang, L., & Shi, Y.* (2020, April). Positioning accuracy assessment of a commercial RTK UAS. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V (Vol. 11414, p. 1141409). International Society for Optics and Photonics.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Li, J., Schachtman, D.P., & Shi, Y. (2020, July 13-15). A case study on applying UAV thermal, multispectral, and RGB imagery in phenotyping sorghum drought tolerance. In ASABE AIM 2020, ASABE, Online. American Society of Agricultural and Biological Engineers.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Campbell MT, Grondin A, Walia H, Morota G. Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice (Oryza sativa). 2020, Journal of Experimental Botany
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Placido DF, Sandhu J, Sato SJ, Nersesian N, Quach T, Clemente TE, Staswick P, Walia H. The Lateral Root Density gene regulates root growth during water stress in wheat. 2020, Plant Biotechnology Journal
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 3. Baba T, Momen M, Campbell MT, Walia H, Morota G Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. 2020, PLOS One
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 4. Impa SM, Vennapusa AR, Bheemanahalli R, Sabela D, Boyle D, Walia H, Jagadish SVK. High night temperature induced changes in grain starch metabolism alters starch, protein, and lipid accumulation in winter wheat. 2020, Plant Cell and Environment
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Saluja M, Zhu F, Yu H, Walia H, Sattler S. Loss of COMT activity reduces lateral root formation and alters the response to water limitation in sorghum. 2020, New Phytologist
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Das Choudhury, S., Maturu, S., Samal, A., and Awada, T. (2020). Leveraging image analysis to compute 3D plant phenotypes based on voxel-grid plant reconstruction. Frontiers in Plant Science, Dec. https://doi.org/10.3389/fpls.2020.521431.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Samal, A., Choudhury, SD., and Awada, T. (2020). Image-Based Plant Phenotyping: Opportunities and Challenges. In: Intelligent Image Analysis for Plant Phenotyping, eds. Samal, A., Choudhury, SD. pp. 3-24, CRC Press.
  • Type: Books Status: Published Year Published: 2020 Citation: Intelligent Image Analysis for Plant Phenotyping 1st Edition by Ashok Samal and Sruti Das Choudhury (Editors). 2020. CRC Press. SBN-13: 978-1138038554. ISBN-10: 1138038555


Progress 10/01/18 to 09/30/19

Outputs
Target Audience:The target audience includedpeers, professionals, graduate and undergraduate students, and private and public sectors in the areas of areas of plant breeding, agronomy, plant physiology, agricultural and biological engineering, computer vision, artificial intelligence, and sensors development. Team members, postdoctoral fellows and graduate students presented their research findings at professional meetings and engaged with peers in other universities through invited talks nationally and internationally. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project provided support to several professionals, training to undergraduate and graduate students, and professional development to post-doctoral fellows and visiting scientists. Our team also repond to inquiries about facilities development. How have the results been disseminated to communities of interest?Principle investigators, students and posdocs presented findings at professional meetings. Pis were invited speakers at several conferences and workshops in the US and elsewhere, and have incoportated findings in classroom materials, and offered tours of the facilities What do you plan to do during the next reporting period to accomplish the goals?1. Continue to make progress on the three objectives 2. Submit additional grant proposals to secure funding for this project 3. Mentor undergraduate and graduate students 4. Provide professional opportunities to postdocs and professionals 5. Disseminate findings

Impacts
What was accomplished under these goals? The goal of this project is to advance high throughput plant phenotyping solutions, and test techniques along a spatial and temporal continuum of plant growth conditions ranging from the controlled greenhouse environment to field plots, to better understand the genotype by environment interactions. Developent ofinnovative, integrative systems approach is imperative to addressing current and future challenges related to global food security using High Throughout Plant Phenomics. Improvements in agricultural management practices, technological advancements, and breeding programs are keys to addressing these challenges considering climate uncertainties and change. High Throughput Phenotyping Platforms (HTPP) - e.g., the LemnaTec Scanalyzer system, offer the opportunity to integrate proximal remote-sensing and imaging methods, together with abiotic/biotic environmental data acquisition, and selected plant responses, to better understand the genotype x environment (GxE)interactions. Objective 1. Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field.In 2019, We developed pipelines for image acquisition and analysis using visible and hyperstpectral cameras for economically important crops. Innovative techniques using artificial intelligence and machine learning to developed to derive growth and biophysical traits in plants using 2D and 3D images. We completed a number of experiments at UNL's high throughput phenotyping greenhouses using maize, soybean, and Hemp plants. The plants were subject to water treatments (drought vs. well-watered) and images were captured on a daily basis using the 5 imaging modules available in the greenhouse. In addition to images, ground truth measurements including leaf area, leaf fresh and dry weight, leaf chlorophyll concentration, light-adapted fluorescence, and leaf macro- and micronutrient concentrations were taken to calibrate / validate the image data. The response of these variables to the drought stress was analyzed and investigated. The impact is that these experiments provided useful calibration models to related image data to the ground truth data for these different crop species, and these calibration models can be readily available for other researchers for their own research. For the field study, wesuccessfully operated the NU-Spidercam system at UNL in 2019. Plot-scale phenotyping data of four different crop species (soybean, maize, sorghum, and tobacco) were captured for the entire growing season, with a measurement frequency of twice per week. The plot scale image data were processed with custom-developed pipelines to extract canopy height, canopy temperature, vegetation coverage, and a few key vegetation indices. These extracted crop traits were further used to build growth curves in terms of height and ground cover, as well as temporal dynamics of canopy temperature. We were also successful in extracting plot-scale solar induced fluorescence signal using the canopy reflectance measurements from the NU-Spidercam. Objective 2. Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems.In 2019, we tested the performance of the UAV-based RGB and multispectral imaging system focusing on wheat and other small grains, soybean, and sorghum phenotyping with more completed data collection over their growing seasons mainly on the breeding sites near Lincoln, NE. The specific breeding objectives were the grain yield of hybrid wheat, the growth pattern of other small grains, the deficiency chlorosis of soybean, and the water and nitrogen stresses of sorghum. Preliminary results were very promising showing that the field-based robotic system (in this case, UAVs) can be used complementarily with the greenhouse and other HTTP systems to extend the phenotyping capability to large geographic regions. Two manuscripts were completed, one published, one submitted, and a couple of conference presentations were delivered by the students. The system needs to be tested in more years under various breeding conditions. In addition, we plan to adapt and test the performance of more latest sensing technologies such as thermal sensing with demonstrated performance in the greenhouse under the field condition with the UAV systems. We have already integrated the thermal sensing system and conducted preliminary tests in field. Objective 3.Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques.Models were developed with classical statistical models and state-of-the-art machine/deep learning for predicting grain yield of winter wheat, and classifying the severity levels of soybean IDC (see publications).?

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Ge, Y., Atefi, A., Zhang, H., Miao, C., Ramamurthy, R.K., Sigmon, B., Yang, J., Schnable, J.C., 2019. High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel. Plant Methods 15, 66.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Das Choudhury, D., Samal, A., and Awada, T. 2019. Leveraging image analysis for high-throughput plant phenotyping. Frontiers in Plant Science, 10:508. https://doi.org/10.3389/fpls.2019.00508.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Bai, G., Ge, Y., Scoby, D., Leavitt, B., Stoerger, V., Kirchgessner, N., Irmak, S., Graef, G., Schnable, J., Awada, T., 2019. NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research. Computers and Electronics in Agriculture 160, 71-81.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Paul P, Dhatt BK, Sandhu J, Hussain W, Irvin L, Morota G, Staswick P, Walia H. Divergent phenotypic response of rice accessions to transient heat stress during early seed development. 2019, Plant Direct, doi: 10.1002/pld3.196.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Sandhu J, Zhu F, Paul P, Gao T, Dhatt BK, Ge Y, Staswick P, Yu H, Walia H. PI-Plat: A high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits. 2019, Plant Methods, doi: 10.1186/s13007-019-0545-2.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Dhatt BK, Abshire N, Paul P, Hasanthika K, Sandhu J, Zhang Q, Obata T, Walia H. Metabolic Dynamics of Developing Rice Seeds Under High Night-Time Temperature Stress. 2019, Frontiers of Plant Science, doi: 10.3389/fpls.2019.01443.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Momen M, Campbell MT, Walia H, Morota G. Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines. 2019, G3, doi: 10.1534/g3.119.400346.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Gao T, Sun J, Zhu F, Doku H, Pan Y, Walia H, Yu H. Plant Event Detection from Time-Varying Point Clouds. 2019, IEEE International Conference on Big Data.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Zhu F, Pan Y, Gao T, Walia H, Yu H. Interactive Visualization of Time-Varying Hyperspectral Plant Images for High-Throughput Phenotyping. 2019, IEEE International Conference on Big Data
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: 1) Li, J., Veeranampalayam-Sivakumar, A. N., Bhatta, M., Garst, N. D., Stoll, H., Baenziger, P. S., ... & Shi, Y. (2019). Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery. Plant methods, 15(1), 123.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: 2) Chen, D., Shi, Y., Huang, W., Zhang, J., & Wu, K. (2018). Mapping wheat rust based on high spatial resolution satellite imagery. Computers and Electronics in Agriculture, 152, 109-116.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: 3) Guo, S., Li, J., Yao, W., Zhan, Y., Li, Y., Shi, Y. (2019). Distribution characteristics on droplet deposition of wind field vortex formed by multi-rotor UAV. PloS one, 14(7): e0220024.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: 4) Li, J., Shi, Y., Lan, Y., & Guo, S. (2019). Vertical distribution and vortex structure of rotor wind field under the influence of rice canopy. Computers and Electronics in Agriculture, 159, 140-146.
  • Type: Theses/Dissertations Status: Published Year Published: 2019 Citation: 1) Li, J. (2019). Unmanned aerial vehicle data analysis for high-throughput plant phenotyping. MS thesis. University of Nebraska-Lincoln. https://digitalcommons.unl.edu/biosysengdiss/88/
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 1) Li, J., Bhatta, M., Garst, N. D., Stoll, H., Veeranampalayam-Sivakumar, A. N., Baenziger, P. S., ... & Shi, Y. (2019). Principal Variable Selection to Explain Grain Yield Variation in Winter Wheat from UAV-derived Phenotypic Traits. In 2019 ASABE Annual International Meeting (p. 1). Boston, Massachusetts. American Society of Agricultural and Biological Engineers.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: 2) Chen, D., Wang, L., Zhang, J., Adams-Selin, R., Hein, G. L., Shi, Y. (2019). Investigate the spatial relationship between wheat streak mosaic infection and pre-harvest hail by using archived satellite imagery and meteorological data. In 2019 ASABE Annual International Meeting (p. 1). Boston, Massachusetts. American Society of Agricultural and Biological Engineers.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Taity Changa, Anastasios Mazis, Sruti Choudhury, Jane Okalebo, Jeremy Hiller, Patrick Morgan, n Gregory Sword, Harkamal Walia, Tala Awada. 2019. High Throughput Assessment of the Highly Responsive Physiological Traits in Cotton Under Drought Stress. ASA-CSSA-SSSA International Annual Meeting | Nov. 10-13 | San Antonio, Texas.


Progress 10/01/17 to 09/30/18

Outputs
Target Audience:The target audience includes peers, professionals, graduate and undergraduate students, and private and public sectors in the areas of areas of plant breeding, agronomy, plant physiology, agricultural and biological engineering, computer vision, artificial intelligence, and sensors development. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project provided support to several professionals, training to undergraduate and graduate students, and professional development to post-doctoral fellows and visiting scientists. Our team also repond to inquiries about facilities development. How have the results been disseminated to communities of interest?Principle investigators, students and posdocs presented findings at professional meetings. Pis were invited speakers at several conferences and workshops in the US and elsewhere, and have incoportated findings in classroom materials, and offered tours of the facilities. What do you plan to do during the next reporting period to accomplish the goals?1. Continue to make progress on the three objectives 2. Submit additional grant proposals to secure funding for this project 3. Mentor undergraduate and graduate students 4. Provide professional opportunities to postdocs and professionals 5. Disseminate findings

Impacts
What was accomplished under these goals? Development of innovative, integrative systems approach is imperative to addressing current and future challenges related to global food security. Improvements in agricultural management practices, technological advancements, and breeding programs are keys to addressing these challenges considering climate uncertainties and change. High Throughput Phenotyping Platforms (HTPP) - e.g., the LemnaTec Scanalyzer system, UAVs and Spidercam Field Phenomics offer the opportunity to integrate proximal remote sensing and imaging methods, together with abiotic/biotic environmental data acquisition, and selected plant responses, to better understand the genotype x environment (GxE) interactions. The goal of this project is to advance high throughput plant phenotyping solutions, and test techniques along a spatial and temporal continuum of plant growth conditions ranging from the controlled greenhouse environment to field plots, to better understand the genotype by environment interactions. Objective 1. Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field. In 2018, we developed new software for image analysis and release image databases for research purposes using the controlled environment Lemnatec System (e.g., https://plantvision.unl.edu/software, listed publications) that focus on using artificial intelligence and neural networks to detect leaf emergence and senescence, plant growth and development, plant architecture and responses to abiotic stresses in corn, rice, wheat and sorghum. These indices were derived from visible and/or hyperspectral cameras. We also conducted test trials on the newly developed field phenomic facility (Spidercam cable suspended system, 1 acre facility) using corn and soybean varieties of different and known morphological and physiological traits. We have been successful in using proximal optical traits to derive plant temperature, seasonal and diurnal changes in leaf optical traits, growth variable and functional traits (see publication list). Objective 2. Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems. In 2018, we used UAV-based phenotyping systems with RGB and multispectral sensors and tested them over sorghum and corn fields. The phenotypic traits that can be characterized so far are canopy morphological traits including plant height and canopy cover, and canopy spectral traits including spectral reflectance in blue, green, red, red edge and near-infrared regions and the derived vegetation indices. We have started working with the wheat breeding team at Nebraska to establish soil/plant/microclimate sensor network in the field. We have used soil electrical conductivity survey to characterize the variability of soils in the field, which will then determine how the sensor nodes are distributed in the field. We continue our work to improve the "Cart" field phenotyping platform (cart). The platform was tested extensively in different Nebraska environments on a number of crops including wheat, soybean, maize, sorghum and camelina. Analysis of the phenotyping data are ongoing. We are processing the data and developing improved methods and algorithms for future experiments. Objective 3. Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques. We developed the NU-Spidercam platform at University of Nebraska's ENREC. This is the third phenotyping infrastructure at UNL that will facilitate the translational research from lab and greenhouse to the field. We are also conducting research on identifying traits that can be scalable across various phenomics platforms for forward and backward breeding purposes. We are researching scalability of traits from leaf level optical traits, to Lemantec Platform (controlled environment), backpack in house system (plot level), Spidercam System (plot to 1 acre), UAV (field), and aircraft (field to landscape). Ground trothing of phenotypic data are being manually measured or destructively measured in the lab including plant height, nitrogen and chlorophyll contents and biomass yield. Members of the team were successful in securing funding from NSF, NIFA, Corn, wheat and Soybean boards, as well as internal sources at UNL to support research efforts.

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Campbell, M., Walia, H., Morota, G. (2018). Utilizing random regression models for genomic prediction of a longitudinal trait derived from high?throughput phenotyping. Plant Direct(2018;2:111).
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Thapa, S., Zhu, F., Walia, H., Yu, H., Ge, Y. (2018). A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum. Sensors, 18(4).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Zhu, F., Thapa, S., Gao, T., Ge, Y., Walia, H., Yu, H. (2018). 3D Reconstruction of Plant Leaves for High-Throughput Phenotyping. IEEE Big Data Conference.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Yuan, W., Li, J., Bhatta, M., Shi, Y., Baenziger, P. S., Ge, Y. (2018). Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS. MDPI Sensors, 18(11), 3731.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Begcy, K., Sandhu, J., Walia, H. (2018). Transient Heat Stress During Early Seed Development Primes Germination and Seedling Establishment in Rice. Frontiers in Plant Science(9:1768).
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2019 Citation: Campbell, M., Walia, H., Morota, G. (in press). Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits. To appear in Plant Genome.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Bai, F., Jenkins, S., Yuan, W., Graef, G., Ge, Y. (2018). Field-based scoring of soybean iron deficiency chlorosis using RGB imaging and statistical learning. Frontiers in Plant Science, 9, 1002.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2018 Citation: Bai, F., Ge, Y., Leavitt, B., Gamon, J., Qi, Ge Y., Awada, T. N., Graef, G., Irmak, S., Schnable, J., Scoby, D., Stoerger, V.2018. AGU Meeting, Washington DC, Capturing diurnal variation of phenotypic traits for breeding plots using NU-Spidercam.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Thapa, S., Ge, Y., Zhu, F., Yu, H., Walia, H. 2018. ASABE Annual International Meeting, Detriot, MI, 360-degree view multi-spectral point cloud generation for the characterization of plant morphological and chemical traits.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Pandey, P., Ge, Y., Schnable, J., 2018. ASABE Annual International Meeting, Detriot, MI, Hyperspectral imaging combining multivariate modeling enable nondestructive analysis of cell wall composition in sorghum.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Atefi, A., Ge, Y., Pitla, S., Schnable, J., 2018. ASABE Annual International Meeting, Detriot, Integration of a plant phenotyping robotic system with LemnaTec high-throughput plant phenotyping system.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Li J., Shi Y., Veeranampalayam-Sivakumar A.N., and Schachtman D.P. 2018. Elucidating Sorghum Biomass, Nitrogen and Chlorophyll Contents With Spectral and Morphological Traits Derived From Unmanned Aircraft System. Frontiers of Plant Science. https://doi.org/10.3389/fpls.2018.01406 .
  • Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Awada, T. (2018). Role of high throughput plant phenotyping and its adoption for addressing current and emerging issues in agricultural research, 3rd Annual Symposium, Plant Phenotyping and Imaging Research Centre. October 17-18, Saskatoon, Canada.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Awada, T., Awada, L., and Das Choudhurry, S. (2018). Role of high throughput plant phenotyping and its adoption for addressing current and emerging issues in agricultural research. 3rd international plant and algal phenomics meeting (IPAP), August 26-29, Prague.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2018 Citation: Das Choudhurry, S., Samal, A., and Awada, T. (2018). Holistic and Component Plant Phenotyping Analysis using Visible Light Image Sequence. Phenome 2018, February 14-17, Tucson AR.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Das Choudhury, S., Bashyam, S., Qiu, Y., Samal, A., and Awada T. (2018). Holistic and component plant phenotyping using temporal image sequence. Plant Methods. 14:35.


Progress 10/01/16 to 09/30/17

Outputs
Target Audience:Target audience included faculty from inside and outside institutions, students, and professional community and the public. Findings and techniques were incorporated in lectures targeted to graduate and undergraduate students, and were presented to peers in professional meetings. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? This project provided support to several professionals, training to undergraduate and graduate students, and professional development to post-doctoral fellows and visiting scientists How have the results been disseminated to communities of interest?Principle investigators, students and posdocs presented findings at professional meetings. Pis were invited speakers at several conferences and workshops in the US and elsewhere, and have incoportated findings in classroom materials, and offered tours of the facilities. What do you plan to do during the next reporting period to accomplish the goals?1. Continue to make progress on the three objectives 2. Submit additional grant proposals to secure funding for this project 3. Mentor undergraduate and graduate students 4. Provide professional oportunities to postdocs 5. Dissiminate findings

Impacts
What was accomplished under these goals? Development of an innovative, integrative systems approach is imperative to addressing current and future challenges related to global food security. Improvements in agricultural management practices, technological advancements, and breeding programs are keys to addressing these challenges considering climate uncertainties and change. High Throughput Phenotyping Platforms (HTPP) - e.g., the LemnaTec Scanalyzer system, offer the opportunity to integrate proximal remote-sensing and imaging methods, together with abiotic/biotic environmental data acquisition, and selected plant responses, to better understand the genotype x environment (GxE) interactions. The goal of this project is to advance high throughput plant phenotyping solutions, and test techniques along a spatial and temporal continuum of plant growth conditions ranging from the controlled greenhouse environment to field plots, to better understand the genotype by environment interactions. Objective 1 Investigate accuracy and validity of phenotypic characterization using HTPP under greenhouse conditions and in the field In 2017, we worked on the instrumentation, sensor integration and motion planning and control of the Spidercam system. The Spidercam system was successfully operated in Year 1. Sensor data were also successfully verified with the ground truth measurements. Starting from Year 2, we will use the Spidercam system for experimentation and data collection. Objective 2 Determine whether single plant phenotyping technique using field robots can be an acceptable alternative to the expensive greenhouse and field HTTP systems In 2017, we continue work to improve this field phenotyping platform (cart). The platform presently included the following plant sensor modules: (1) IR radiometers, (2) RGB cameras, (3) portable spectrometers with fiber optics, (4) a scanning LIDAR. In addition, the platform also incorporated microenvironment sensors for solar radiation, air temperature, and relative humidity measurements. A differential GPS was also included. The platform was tested extensively in different Nebraska environments on a number of crops including wheat, soybean, maize, sorghum and camelina. Analysis of the phenotyping data were ongoing. We also established a wheat, millet, rice experiments in the greenhouse to assess their responses to water and temperature stresses using highthroughput and tranditional methods. We are processing the data and developing improved methods to conducting future experiments. Objective 3 Investigate the feasibility of scaling results from single plants and greenhouse to field plots and develop models to forecast performance and yield using crop modeling techniques. In 2017, we conducted pilot study and constructed five in-field plant and soil sensor nodes and launched them in a maize (3 nodes) and soybean field (2 nodes) at UNL's ARDC. Each sensor node consisted of an underground section and an above ground section. The underground section included three soil sensors (Decagon Devices 5TM) at depths of 15, 45, and 75 cm. The sensor simultaneously measures volumetric water content (VWC), soil temperature, and electrical conductivity. The above ground section included an infrared thermometer (Apogee Instruments SI-111) to measure canopy temperature, a passive canopy NDVI and PRI sensor (Decagon Devices' Spectral Reflectance Sensor), and a RGB camera controlled by Raspberry PI and Arduino. Sensor data were recorded by a data logger (Campbell Scientific CR1000 or CR10x) at 1-min interval. The season-long test of these five sensor nodes suggested they functioned well; and we will bring the sensor network to the full scale in Year 3, as specified in the proposal. Members of the team were successful in securing funding from NSF, NIFA, Corn and Soybean boards, as well as internal sources at UNL to support research efforts.

Publications

  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Liang, Z., Pandey, P., Stoerger, V., Xu, Y., Qiu, Y., Ge, Y., Schnable, J. (2017). Conventional and hyperspectral time-series imaging of maize lines widely used in field trials. GigaScience, gix117, https://doi.org/10.1093/gigascience/gix117.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Bai, G., Blecha, S., Ge, Y., Walia, H., Phansak, P. (2017). Characterizing wheat response to wheat limitation using multispectral and thermal imaging. Transactions of the ASABE, 60(5), 1457-1466.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Pandey, P., Ge, Y., Stoerger, V., Schnable, J. (2017). High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Frontiers in Plant Science 8, https://doi.org/10.3389/fpls.2017.01348.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Schnable, J., Ge, Y. (Presenter & Author), 2017 AGU, New Orleans, LA, "From plots to plants to traits", (December 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Ge, Y. (Presenter & Author), Bai, G., Awada, T. N., Stoerger, V., Scoby, D., Graef, G., Schnable, J., 2017 AGU, New Orleans, LA, "High throughput plant phenotyping field facility at University of Nebraska-Lincoln and the first year experience", (December 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Atefi, A. (Presenter & Author), Ge, Y. (Author Only), Pitla, S., Stoerger, V., ASABE Annual International Meeting, Spokane, WA, "Development of a robotic system to grasp plant leaves for phenotyping", (July 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Bai, G. (Presenter & Author), Ge, Y. (Author Only), Irmak, S., Awada, T. N., ASABE Annual International Meeting, Spokane, WA, "High throughput field phenotyping facility at University of Nebraska-Lincoln". (July 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Ge, Y. (Presenter & Author), 2017 Water for Food Global Conference, DWFI, Nebraska, NE, "Advanced imaging for phenotyping water-related crop traits", (April 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Ge, Y. (Presenter & Author), Predictive Crop Design: Genome to Phenome Symposium, Nebraska EPSCOR, Lincoln, NE, "Engineering instruments and robotics for high throughput plant phenotyping", (April 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Ge, Y. (Presenter & Author), ISU Plant Breeding Symposium, Iowa State University, Ames, IA, "High throughput plant phenotyping in greenhouse and field - Translational pipelines from gene discovery to crop improvement", (March 2017).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Awada, T. (2017). Application of plant phenotyping in agricultural research, Invited Speaker, 2nd Annual Symposium, Plant Phenotyping and Imaging Research Centre. Saskatoon, Canada. June 20-22.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Awada, T. (2017). Plant phenomics in agroecosystems research. Chinese Academy of Sciences, Naiman Desertification Research Station. Inner Mongolia, China. September 9.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Awada, T., Gomez, R.-L., Bacher, H., Das Choudhury, S., Walia, H., Ge, Y., Stoerger, V. (2017). High throughput plant phenotyping application in addressing current and emerging issues in agricultural research. 10th Annual International Symposium on Agriculture, Athens, Greece. July 13-15.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Choudhury, S., Goswami, S., Bashyam, S., Samal, A., and Awada, T. (2017). Automated stem angle determination for temporal plant phenotyping analysis. ICCV workshop on Computer Vision Problems in Plant Phenotyping, Venice, Italy.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Choudhury, S.D., Bashyam, S., Samal, A., and Awada, T. (2017) Automated leaf tracking using multi-view image sequences of maize plants for leaf-growth monitoring. AGU Fall Meeting, New Orleans, USA, December 15.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Campbell, M. T., Bandillo, N., Shiblawi, A., Sharma, S., Liu, K., Du, Q., Schmitz, A. J., Zhang, C., V�ry, A. A., Lorenz, A. J., Walia, H. (2017). Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content. PLOS PLOS Genetics.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Razzaque, S., Haque, T., 3, E. S., Rahman, M. S., Biswas, S., Schwartz, S., Ismail, A. M., Walia, H., Juenger, T. E., Seraj, Z. (2017). Reproductive stage physiological and transcriptional responses to salinity stress in reciprocal populations derived from tolerant (Horkuch) and susceptible (IR29) rice. Nature Publishing Group Scientific Reports, 7.