Source: WASHINGTON STATE UNIVERSITY submitted to NRP
PHENOMICS AND MODELING ENABLED DECISION SUPPORT FOR CLIMATE-ADAPTED WHEAT GERMPLASM DEVELOPMENT
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1028108
Grant No.
2022-67013-36426
Cumulative Award Amt.
$649,996.00
Proposal No.
2021-07631
Multistate No.
(N/A)
Project Start Date
Jan 1, 2022
Project End Date
Dec 31, 2025
Grant Year
2022
Program Code
[A1141]- Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production
Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001
Performing Department
Biological Systems Engineering
Non Technical Summary
Crop improvement efforts are critical to sustain food production to feed global population and combat adverse effects of climate change. In a typical crop improvement/breeding program, germplasms are evaluated at multiple locations for multiple years to capture crop responses across multiple G x E scenarios prior to a release of a variety for commercial production. With very low selection efficiency (<1% overall), the crop improvement process remains resource intensive and inefficient. With phenomics facilitating objective assessment and modeling offering opportunities to evaluate performance across a variety of environments, integrating phenomics and modeling into the breeding cycle has the potential to increase the genetic gain via data-informed improved selection. In this project, we propose to develop and demonstrate a decision support system involving the integration of phenomics and modeling for informed climate-adapted crop germplasm selection in breeding programs. In this project, heterogenous data will be acquired from internet of things (IoT) based remote sensing network, unmanned aerial system (UAS), and low-orbiting satellite (LOS) imaging systems, alongside microclimate data, which will be integrated with the data mining approaches. The project will utilize wheat (Triticum aestivum, spring and winter wheat) as a model crop. The phenomics-data driven models will then be developed to predict the performance of crop germplasms over current and long-term projections of climatic conditions for the wheat-producing area of the US Pacific Northwest.
Animal Health Component
70%
Research Effort Categories
Basic
10%
Applied
70%
Developmental
20%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011549202040%
4021549208050%
4021549108110%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms; 402 - Engineering Systems and Equipment;

Subject Of Investigation
1549 - Wheat, general/other;

Field Of Science
2020 - Engineering; 1081 - Breeding; 2080 - Mathematics and computer sciences;
Goals / Objectives
Crop improvement efforts are critical to sustain food production to feed global population and combat adverse effects of climate change. In a typical crop improvement/breeding program, germplasms are evaluated at multiple locations for multiple years to capture crop responses across multiple G x E scenarios prior to a release of a variety for commercial production. Recent advances in phenomics - high-throughput multi-dimensional phenotyping through sensing and automation - have the potential to facilitate objective and quantitative assessment of crop performance, thereby allowing informed and confident selection of high performing varieties in breeding programs through phenomics-assisted selection. Similarly, modeling is a means to understand performance across a variety of environments - beyond what is feasible via field experiments - in a cost- and time-efficient manner, thus complementing field experiments. Integrating phenomics and modeling into the breeding cycle has the potential to increase the genetic gain via data-informed improved selection accuracy towards enhanced yield potential. Improved identification of superior genotypes across a wide range of environments will also enhance the selection process. Therefore, the major goal in this project is to develop and demonstrate a decision support system involving the integration of phenomics and modeling approaches for an informed climate-adapted crop germplasm selection in breeding programs.
Project Methods
In this project, high spatial/temporal resolution heterogenous data will be acquired from internet of things (IoT) based Arduino sensors, unmanned aerial systems (UAS), and low-orbiting satellites (LOS) multispectral imagery. This multi-scale heterogenous data will be integrated with advanced data science approaches for extracting phenotypic traits and robust crop model development for predicting short- and long-term crop responses to changing environmental conditions, to develop performance evaluation and selection decision support tools. Two trials will be utilized for this aspect, which are the variety testing and drought stress trials. The findings will be validated using early generation and other field trials. The phenomics data (UAS and/or LOS) will be used to provide realistic crop phenotype parameterization during crop model development for accurate crop performance prediction in variable and changing climate conditions. The project will utilize a spring and winter wheat (Triticum aestivum) breeding program in the Pacific Northwest as a model system, taking advantage of our extensive germplasm collections.

Progress 01/01/24 to 12/31/24

Outputs
Target Audience:The project efforts through technical and outreach presentations, and field day talks reached researchers from other universities/private institutes (national/international), undergraduate and graduate students, and growers. The specific activities for the year 2024 are listed in the Products and the Dissemination of Results sections. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project involved several undergraduate and graduate students working on the project. In addition, PIs (working on breeding, phenomics, and crop modeling) and technical support researchers were also actively involved, primarily during project meetings, planning of field trials, and data acquisition. How have the results been disseminated to communities of interest?The results have been disseminated to communities through field days and other outreach talks. In addition, the project plans and expected outcomes were shared with about hundreds of growers by wheat breeders through participation in different field days. What do you plan to do during the next reporting period to accomplish the goals?We have concluded the field trials. In 2025, we will aim to take technology and tools to the grower's field. In addition, the phenomics and modeling teams will evaluate multimodal data towards accomplishing the project objectives.

Impacts
What was accomplished under these goals? The 2024 field season included variety trials for winter and spring wheat at Pullman in WA, and Lind and Ritzville in WA, which represent two distinct precipitation zones in the Pacific Northwest, and irrigation trials in Othello, Washington. We selected approximately 6-8 wheat varieties for the Othello irrigation trials based on historical data, wheat variety classifications, and their potential for future production, similar to the selections made in the 2022 and 2023 field seasons. The winter wheat trials employed two irrigation schemes, while the spring wheat trials utilized three different irrigation rates to induce drought stress. Unmanned aerial system (UAS) data, including multispectral and thermal imagery, were collected for both winter and spring wheat trials across all four locations, with data gathered at six to eight-time points depending on the growth cycle. Regarding the Internet of Things (IoT)-based sensors, we integrated a prototype Raspberry Pi-based sensor equipped with multispectral cameras, including RGB and near-infrared (NIR), with 160x120 resolution and 32x24 resolution imaging thermal cameras to assess the quality of thermal data. This enhanced sensor system was designated AGIcam+. We collected data at three-time points each day from the winter wheat irrigation trial, from the start of the growing season until just before harvest (May-August). Additionally, we collected soil moisture data using neutron probe, LiCOR data, and other agronomic information in both spring and winter irrigation trials in Othello. In the 2024 field season, we connected the AGIcam+ system with internet and tested two approaches - a WiFi extension and cellular network. In addition, data processing algorithms were developed for segmentation and other applications. The RoboFlow and YOLOv5 object detection models were applied on the images from the IoT system for automatically identifying calibrated reference panels for radiometric correction. The preliminary results indicated an accuracy of nearly 100% in detecting the panel in each image. Additionally, the results from the LiCOR data (2024) on the spring wheat irrigation trial demonstrated more favorable data compared to the winter wheat irrigation trial. In most spring wheat varieties, the increased irrigation rate showed higher CO2 assimilation rates, particularly two weeks after the heading stage. In winter wheat varieties, higher stomatal conductance was observed in well-watered conditions during the heading stage. In spring wheat, the stomatal conductance of plants under full irrigation was higher than partial and low irrigation rates at two weeks after heading. We also developed our first version of a machine learning model to predict crop performance across diverse environments utilizing spring wheat breeding trial data from Washington State. This work was presented at the 2024 American Geophysical Union's Fall meeting. We are currently working on improving the model and publishing the work. We are also working on integrating biophysical and machine learning models to improve model performance. In addition, the work predicting crop phenological stages (described in the previous report) was utilized as part of a manuscript related to the temporal dynamics of wheat-associated bacterial communities that just got accepted for publication in the PhytoFrontiers journal. Overall, we reported the shift of the plants' microbial community over multiple seasons, potentially resulting from host plant selection and environmental drivers such as soil moisture.

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Herr, A.W., Campbell, K.G., Li, X. and Carter, A.H., 2024. Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials. The Plant Phenome Journal, 7(1), p.e70007.
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Sandhu, K.S., Burke, A.B., Merrick, L.F., Pumphrey, M.O. and Carter, A.H., 2024. Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat. Frontiers in Plant Science, 15, p.1460353.
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Herr, A., Schmuker, P. and Carter, A.H., 2023. Large-scale breeding applications of UAS enabled genomic prediction. The Plant Phenome Journal 7:e20101 doi:10.1002/ppj2.20101
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Montesinos-L�pez, O.A., Herr, A.W., Crossa, J., Montesinos-L�pez, A. and Carter, A.H., 2024. Enhancing winter wheat prediction with genomics, phenomics and environmental data. BMC Genomics, 25(1), p.544.
  • Type: Other Journal Articles Status: Accepted Year Published: 2024 Citation: Yang, M. M., Schlatter, D., Letourneau, M., Wen, S., Mavrodi, D., Mavrodi, O., Thomashow, L., Kandlati, E., Rajagopalan, K., Weller, D., and Paulitz, T., Eight years in the soil: Temporal dynamics of wheat-associated bacterial communities under dryland and irrigated conditions. PhytoFrontiers Journal.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Paiboonvarachat, C., Pumphrey, M.O., Carter, A.H., Rajagopalan, K., Zhang, C., Sangjan, W., and Sankaran, S. 2024. Variability in crop responses between varieties as a function of environment in multi-environment wheat breeding trials. S1069: Research and Extension for Unmanned Aircraft Systems (UAS) Applications in U.S. Agriculture and Natural Resources, Multistate Research Project Meeting, Bozeman, MT, 13-14 June 2024.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Norouzi, E., Rajagopalan, K., Sankaran, S., and Noorazar, H. 2024. Impact of environmental factors on key performance aspects of soft white spring wheat. Conference poster presentation, AGU Fall 2024 Fall Meeting., Washington D.C. https://doi.org/10.13140/RG.2.2.35949.09449.
  • Type: Other Status: Other Year Published: 2024 Citation: Paiboonvarachat, C., Pumphrey, M.O., Carter, A.H., Rajagopalan, K., Sankaran, S., Zhang, C., and Sangjan, W. 2024. Variability in crop responses between varieties as a function of environment in multi-environment wheat breeding trials. WSU Plant Science Symposium, Pullman, WA, 18 March 2024.
  • Type: Other Status: Other Year Published: 2024 Citation: Paiboonvarachat, C., Pumphrey, M.O., Carter, A.H., Rajagopalan, K., Sankaran, S., Zhang, C., and Sangjan, W. 2024. Variability in crop responses between varieties as a function of the environment in multi-environment wheat breeding trials. WSU Biological Systems Engineering Retreat, Pullman, WA, 9 March 2024.


Progress 01/01/23 to 12/31/23

Outputs
Target Audience:The project efforts through technical and outreach presentations, and field day talks reached researchers from other universities/private institutes (national/international), undergraduate and graduate students, and growers. The specific activities for the year 2023 are listed in the Products and the Dissemination of Results sections. Changes/Problems:None to report. What opportunities for training and professional development has the project provided?The project involved five graduate students, three undergraduate students, and a postdoctoral associate working on the project. In addition, PIs (working on breeding, phenomics, and crop modeling) and technical support researchers were also actively involved, primarily during project meetings, planning of field trials, and data acquisition. How have the results been disseminated to communities of interest?The results have been disseminated to communities through field days and other outreach talks, including podcasts. In addition, the project plans and expected outcomes were shared with about 300+ growers by wheat breeders (Arron Carter and Mike Pumphrey) through participation in eight different field days. What do you plan to do during the next reporting period to accomplish the goals?In the 2024 field season, we will repeat field trials similar to those in the 2022 and 2023 field season, though only two field seasons were proposed. The UAS and other types of data will be collected. In addition, we will implement IoT sensors in winter wheat trails. The modeling team is anticipating further evaluation of historical data to understand and evaluate the performance of the selected wheat varieties over multiple seasons.

Impacts
What was accomplished under these goals? The 2023 field season comprised winter and spring wheat variety trials at Pullman, WA, and Lind, WA (two extreme precipitation zones in Pacific Northwest Washington), and irrigation winter and spring wheat trials in Othello, WA. About 6-8 varieties were selected for the Othello irrigation trials, based on the available historical data, wheat variety classes, and its potential in future wheat production, similar to the 2022 field season. The winter wheat trials had two irrigation schemes, while the spring wheat trials had three irrigation rates to induce drought stress. Unmanned aerial system (UAS) data (multispectral and thermal) were collected from winter and spring wheat trails in all three locations (six to eight-time points depending on the growth cycle). In regard to the internet of things (IoT)-based sensors, our previous prototype of a Raspberry Pi-based sensor with RGB and multispectral camera (available in the lab) with IoT capabilities was integrated with a thermal camera and thermal point sensor, to evaluate the thermal data quality. The sensor system was termed AGIcam+. The data (two to three-time points) were acquired from winter and spring wheat trails around the heading growth stages. In addition, soil moisture (neutron probe), LiCOR data, and other agronomic data were collected. The findings revealed a significant correlation between AGIcam+ and UAV data (vegetation indices and thermal data), particularly pronounced during the heading and post-heading stages. Pearson's correlation coefficients for the normalized difference vegetation index (NDVI) and 95th percentile temperature data exhibited ranges of 0.81-0.88 and 0.81-0.95 (P < 0.01), respectively. The research further included yield prediction models, developed through multivariate linear regression analysis, utilizing data from both systems. These models underscored the parallel efficacy of AGIcam+ with UAVs in yield estimation, as evidenced in the Spring 2023 trial (AGIcam+: R2 = 0.79, RMSE = 891 kg/ha; UAV: R2 = 0.86, RMSE = 719 kg/ha). The data across multiple locations and seasons will be further evaluated, in addition to acquiring data for another season (2024) towards increasing confidence in data interpretation. In regard to crop modeling, we have assembled the target population of environments (TPE) by overlaying the gridded meteorological dataset (gridMET) with the wheat production areas in the Pacific Northwest US. Monthly precipitation, temperature and growing degree days (GDD) from 1980-2022 were used to create the TPE. We are currently updating it with future climate projections. We compared the TPE with the environments associated with prior breeding trials (between 2004 and 2019 across 22 locations and 56 varieties) and found that the trial environments were largely representative of the TPE. The trial data was also used to parameterize variety specific GDD requirements to reach specific plant growth stages (used in Yang et al., under review). A web interface where users can compare one specific environment placed in the context of the TPE was also developed. The intent is to expand this to a platform where breeders can look at the current trial's environment and place it in the context of the full TPE and also identify analogous years, and locations for performance comparisons. We are currently analyzing the prior breeding trial data to quantify the relationship between the environmental variables (GDD, temperature, precipitation, dGDD, diurnal temperature range) and crop performance and traits (yield, protein content, plant height and days to heading) utilizing correlation analysis. The results will be used to develop a predictive model to extrapolate crop performance over diverse environments.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Dixon, L., Bellinger, B. and Carter, A.H., 2023. A gravimetric method to monitor transpiration under water stress conditions in wheat. The Plant Phenome Journal, 6(1), p.e20078. doi:10.1002/ppj2.20078
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Herr, A.W. and Carter, A.H., 2023. Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications. Frontiers in Plant Science, 14. doi:10.3389/fpls.2023.1233892
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Herr, A.W., Adak, A., Carroll, M.E., Elango, D., Kar, S., Li, C., Jones, S.E., Carter, A.H., Murray, S.C., Paterson, A. and Sankaran, S., 2023. Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science, 63(4), pp.1722-1749. doi:10.1002/csc2.21028
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Marzougui, A., McGee R.J., Van Vleet, S., and Sankaran, S. 2023. Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics. Frontiers in Plant Science, 14:1111575. 10.3389/fpls.2023.1111575.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Montesinos-L�pez, O.A., Herr, A.W., Crossa, J. and Carter, A.H., 2023. Genomics combined with UAS data enhances prediction of grain yield in winter wheat. Frontiers in Genetics, 14, p.1124218. doi:10.3389/fgene.2023.1124218
  • Type: Journal Articles Status: Submitted Year Published: 2023 Citation: Yang, M., Schlatter, D., LaTourneau, M., Wen, S., Mavrodi, D., Mavrodi, O., Thomashow, L., Kandlati, E., Rajagopalan, K., Weller, D, and Paulitz, T. Eight years in the soil: Temporal dynamics of wheat-associated bacterial communities under dryland and irrigated conditions. Soil Biology and Biochemistry, Under review, 2023.
  • Type: Other Status: Accepted Year Published: 2023 Citation: Herr, A., and Carter, A. 2023. Can we use UAS imagery to improve genomic selection for grain yield? Texas A&M Corteva Plant Breeding Symposium, College Station, TX, 18 February 2023.
  • Type: Other Status: Other Year Published: 2023 Citation: Carter, A. 2023. Breeding the most elite winter wheat, We Measure the World Podcast Episode 22, Meter Group, 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Herr, A., and Carter, A. 2023. Capturing a more complete picture in plant breeding: Using UAS imagery to improve genomic prediction. National Association of Plant Breeders (NAPB) Annual Meeting, Greenville, SC, 16-20 July 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Herr, A., and Carter, A. 2023. Capturing a more complete picture in plant breeding: Using UAS imagery to improve genomic prediction. ASA-CSSA-SSSA International Annual Meeting, St. Louis, MI, 29 October-1 November 2023.
  • Type: Other Status: Accepted Year Published: 2023 Citation: Herr, A., and Carter, A. 2023. Capturing a more complete picture in plant breeding: Using UAS imagery to improve genomic prediction. Corteva New Frontiers Conference, 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Sangjan, W., Carter, A.H., Pumphrey, M.O., Hagemeyer, K., Jitkov, V., and Sankaran, S. 2023. Effect of UAV and high-resolution satellite imagery plot pixel resolution on assessment of crop performance in spring and winter wheat breeding programs. Paper No. 2301067, 2023 American Society of Agricultural and Biological Engineers Annual International Meeting (ASABE AIM), Omaha, NE, 10-12 July 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Sangjan, W., Pukrongta, N., Carter, A.H., Pumphrey, M.O., and Sankaran, S. 2023. AGIcam: IoT-based multispectral sensor system for real-time and automated in-field crop monitoring. Paper No. 2301064, 2023 ASABE AIM, Omaha, NE, 10-12 July 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Valencia?Ortiz, M., McGee, R., Carter, A.H., and Sankaran, S. 2023. Effect of radiometric correction as a function of UAV flight altitudes on vegetation indices and yield prediction. Paper No. 2300922, 2023 ASABE AIM, Omaha, NE, 10-12 July 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Veloo, K., Valencia?Ortiz, M., Pumphrey, M.O., Carter, A.H., and Sankaran, S. 2023. Multispectral and thermal camera integrated sensor system development and evaluation for in-field crop monitoring for IoT applications. Paper No. 2301202, 2023 ASABE AIM, Omaha, NE, 10-12 July 2023.
  • Type: Other Status: Other Year Published: 2022 Citation: Carter, A. 2022. Breeding wheat in the 21st Century, Washington Wheat Academy, 2022.
  • Type: Other Status: Other Year Published: 2022 Citation: Carter, A. 2022. Sensors, drones and the internet of things, Wheat Life, August 2022.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Carter, A. 2023. Use of Unmanned Aerial Systems in Plant Breeding, Crop Science Society of America Webinar Series [Invited Speaker, Virtual] [Participants: 1100+]
  • Type: Other Status: Other Year Published: 2023 Citation: Carter, A. and Pumphrey, M. 2023. Lind field day presentation, June 2023. [Participants: 300+]
  • Type: Other Status: Other Year Published: 2023 Citation: Herr, A., and Carter, A. 2023. Capturing a more complete picture in plant breeding: Using UAS imagery to improve genomic prediction, WSU Academic Showcase, 2023.
  • Type: Other Status: Other Year Published: 2023 Citation: Herr, A., and Carter, A. 2023. Drone high throughput phenotyping in wheat breeding, WSU Molecular Plant Sciences Department Seminar, 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Sangjan, W., Pukrongta, N., Carter, A. H., Pumphrey, M. O., and Sankaran, S. 2023. IoT-based sensor system for automated in-field monitoring in Washington State, USA, Affordable Phenotyping Workshop, International Plant Phenotyping Network (IPPN), Angers, France, 26-27 June 2023 [Invited Speaker, Virtual] [Participants: 40+].
  • Type: Theses/Dissertations Status: Published Year Published: 2023 Citation: Kesevan Veloo, MS Thesis, Phenomics for evaluating drought tolerance in different wheat varieties towards sustainable crop production.
  • Type: Other Status: Other Year Published: 2023 Citation: Capstone Project, August 2023-May 2024. Automated reference panel detection and plot segmentation using image-based classification approaches.


Progress 01/01/22 to 12/31/22

Outputs
Target Audience:The project efforts through technical and outreach presentations,and field day talks reached researchers from other universities/private institutes (national/international), undergraduate and graduate students, and growers. Changes/Problems:Our field trials needed to be revised due to the limitations in the field conditions. For example, we had to change winter wheat trials to two treatments under drip irrigation, while spring wheat trails have been shifted to a linear irrigation system for better management. In addition, we had to revise the data collection using the IoT-based sensors due to several challenges associated with the IoT-based sensors such as Raspberry Pi outage, sensor costs beyond budget, and installation challenges. What opportunities for training and professional development has the project provided?The project involved three graduate students, one undergraduate student, and one visiting scholar working on phenomics aspects, alongside two graduate students from the modeling research group. In addition, PIs (working on breeding, phenomics, and crop modeling) and technical support researchers were also actively involved, primarily during project meetings, planning of field trials, and data acquisition. How have the results been disseminated to communities of interest?The results have been disseminated to communities through two technical and two outreach talks. In addition, the project plans and expected outcomes were shared with about 350+ growers by wheat breeders (Arron Carter and Mike Pumphrey) through participation in eight different field days. What do you plan to do during the next reporting period to accomplish the goals?In the 2023 field season, we will repeat the field trials similar to those in the 2022 field season. The UAS and other types of data will be collected. In addition, we will implement IoT sensors in winter wheat trails, while acquiring data similar to the 2022 field season in spring wheat trails at Othello, WA. The modeling team will also start exploring the historical data to understand and evaluate the performance of the selected wheat varieties over multiple seasons.

Impacts
What was accomplished under these goals? The 2022 field season comprised winter and spring wheat variety trials at Pullman, WA, and Lind, WA, and irrigation winter and spring wheat trials in Othello, WA. About 6-8 varieties were selected for the Othello irrigation trials, based on the available historical data, wheat variety classes, and its potential in future wheat production. The winter wheat trials had two irrigation schemes, while the spring wheat trials has three irrigation rates to induce drought stress. Unmanned aerial system (UAS) data (multispectral and thermal) were collected from winter and spring wheat trails in all three locations (six to eight-time points depending on the growth cycle). In regard to the internet of things (IoT)-based sensors, our previous prototype of a Raspberry Pi-based sensor with RGB and multispectral camera (available in the lab) with IoT capabilities was integrated with a thermal camera and thermal point sensor, to evaluate the thermal data quality. The data (three-timepoints) were acquired from winter and spring wheat trails around the heading growth stages. In addition, soil moisture (neutron probe), LiCOR data, and other agronomic data were collected. Currently, the data is being processed and analyzed to make changes, if necessary, during the upcoming field season.

Publications

  • Type: Other Status: Published Year Published: 2022 Citation: Sangjan, W., Pukrongta, N., Carter, A. H., Pumphrey, M. O., & Sankaran, S. (2022). Development of IoT-based camera system for automated in-field monitoring to support crop breeding Programs. Authorea Preprints.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Sankaran, S., Sangjan, W., Pumphrey, M. O., Carter, A. H., Jitkov, V., and Hagemeyer, K.E. 2022. Multi-scale multispectral imaging for crop phenotyping applications. 2022 The Asian Federation for Information Technology in Agriculture (AFITA)/ The World Congress on Computer in Agriculture (WCCA), Hanoi, Vietnam, 24-26 November 2022.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Sangjan, W., Carter, A.H., Pumphrey, M.O., Jitkov, V., and Sankaran, S. 2022. Effect of plot pixel resolution on crop performance assessment using features extracted from UAV and high?resolution satellite imagery. Paper No. 2201235, 2022 ASABE AIM, Houston, TX, 18-20 July 2022.
  • Type: Other Status: Other Year Published: 2022 Citation: Sankaran, S. 2022. Capturing crop agronomic traits across different spatial and temporal scales. Joint WSU-Germanys Cluster of Excellence on Plant Sciences (CEPLAS) Seminar Series on Complex Plant Traits, 12 July 2022.
  • Type: Other Status: Other Year Published: 2022 Citation: Sankaran, S. 2022. Internship opportunities at Washington State University. Mathematics, Engineering, Science Achievement (MESA) Winter Orientation, Wenatchee, WA 14 January 2022.