Progress 11/15/16 to 11/14/20
Outputs
Target Audience:The primary target audience for this project are research scientists and plant breeders in the U.S. and internationally. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? Through the project, we have contributed to the training and professional development of two graduate students and two postdoctoral research associates.Each of these students and postdocs have attended multiple research conferences and workshops, given poster and oral presentations and developed professional networks. In addition, two research associates Dr. Xu Wang and Byron Evers acquired FAA Part 107 licenses for UAS remote pilot operations. How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Progress on specific goals and objectives: 1. Develop and deploy robust and scalable unmanned aerial systems with a range of spectral and thermal imaging capabilities; including standardized protocols for routine collection of field-based HTP measurements using UAVs over large wheat breeding nurseries 1.1.Deployment of UAVs in KSU and CIMMYT breeding programs In 2020, multispectral images were collected using DJI Matrice 100 UAVs equipped with Micasense RedEdge-M cameras in 13 trials at 7 locations across Kansas in the K-State wheat breeding program and 10 trials at 3 locations in India in the CIMMYT wheat breeding program. In another CIMMYT wheat breeding program at Obregon, Mexico, high-elevation flights implemented by a fixed-wing airplane carrying a Micasense RedEdge-M camera covered 2 yield trials. During the project year, we collected image datasets with up to 194 flights from targeted locations. Over 250,000 plot-level observations were extracted from the 2020 dataset. 1.2.High-resolution imaging from UAV To generate extremely high resolution (e.g. sub-mm pixel resolution) from a UAV, we have developed and deployed a DJI M600 with DJI Zenmuse X5R 4K video camera recording RAW image frames at 24p. In 2020, 12 flights were implemented to cover 870 plots in a winter wheat association mapping panel for scoring the heading date, the awned/awnless trait, and the spike and spikelet numbers. To match this image data with ground truth labels, we scored the plots for each of the traits, taking visual assessment of heading percentage and heading date, manual counts of spike and spikelet numbers and scoring of awns.This completes a robust dataset of three years of observations combined wit the high-resolution UAV imaging. To identify individual plot from aerial images robustly and automatically, we developed an image processing pipeline.The whole image processing pipeline was implemented using Python and the source code was available online (github.com/CameronAmos/Plot-Identification-Pipeline).The developed pipeline has now been published (see products). 1.3.Hyperspectral imaging from UAV We continue with testing and dataset collection with Headwall Nano Hyperspectral camera (400 - 1000 nm) mounted on DJI M600 UAV.Pilots and technicians in the KSU group have completed additional training for operation. We collected hyperspectral data of the winter wheat experiment fields in both Manhattan and Colby, Kansas, with Headwall Nano Hyperspectral camera. Four sets of hyperspectral data were taken for each experiment field from middle to late June, during the grain filling stage of the wheat.The UAV flew at 100 m above ground at the speed of 2 m/s, with a field of view (FOV) of 13.6 m.The collected raw imagery has a spatial resolution of 2.5 cm.This completes a second year of datasets with the hyperspectral imaging which has been analyzed as described below. 2.Complete foundational work to develop and validate integrated genomic and physiological models for yield prediction; including novel statistical modeling of hyperspectral data 2.2.Hyperspectral prediction modeling To better utilize the UAV-based hyperspectral imagery in wheat yield estimation, we tested three approaches, namely NDVI, chlorophyll potential and partial least squares regression using the hyperspectral data to predict grain yield.While NDVI uses two single bands, chlorophyll potential uses partial spectrum and partial least squares regression uses the full spectrum.Result suggests that while NDVI explained 19% of the yield variability, chlorophyll potential explained 28% of the variability.The coefficient of determination equals 0.43 between measured yield and estimated yield by partial least squares regression.Their mean absolute errors (t/ha) are 0.73, 0.66 and 0.6 respectively.This result implies that it would be more reliable to identify high-yielding wheat genotypes with the full spectrum than with single bands or partial spectrum. 4. Develop robust and efficient analysis pipelines that can facilitate community adoption of field-based HTP and enable in-season selection decisions 4.1.Automated processing pipeline for multi-spectral imaging We have developed the automated pipeline for pre-processing UAV image data from the MicaSense RedEdge(-M) camera, including renaming, geospatial grouping, and radiometric calibration.The automated pipeline generates geo-rectified orthomosaic photos of wheat breeding field from UAV image data, including identification of ground control points for camera position optimization and photogrammetry processing UAV images.The image processing is followed by automated pipeline for extraction plot-level traits (e.g. vegetation indices and canopy height) from orthomosaic photos of wheat breeding field.The pipeline of extracting plot-level trait consists of (1) cropping single-plot images from an orthomosaic of the complete field, (2) converting pixel values to trait values through raster calculation, and (3) summarizing the plot-level trait in each plot-level image.The trait extraction procedure was implemented using Python, and the source code is available online (github.com/xwangksu/traitExtraction). Through further study, we found the plot-level orthomosaic image generated by the photogrammetry process might fail to reflect trait variation that seemed apparent between plot-level raw images, leading to potential loss of information.This lost information during the photogrammetry process could be partially recovered, whereas simply blending the information could undermine the quality of phenotypic data. With that, we refined our processing pipeline to export multiple orthorectified raw UAV images rather than the blended orthomosic image.The complete pipeline was implemented using Python, and the source code is available online (github.com/xwangksu/bip).Following that, multiple plot-level trait observations can be extracted from the orthorectified images rather than only one plot-level trait extracted from the orthomosaic image.Through comparing the plot-level trait extracted from the orthomosaic and orthorectified images, we found strong evidence that the trait collected by UAS remote sensing could be better estimated by extracting observations directly from multiple orthorectified images and using proper models.This study has been completed and recently published in Frontiers in Plant Science (see products). 5.Assess optimal selection strategies for breeding programs 5.1.Yield prediction of small plots The irrigated bread wheat breeding program at CIMMYT utilizes a "selected bulk" scheme between the F3 and F5 generations in which one spike from each selected plant is harvested and bulked with spikes from the same cross to form the next generation.At the F6 stage, individual spikes are selected and promoted individually to the F7 which are sown as unreplicated double or triple rows within 0.7-1m plots in Ciudad Obregón.As yield measurements on these plots are unreliable and unfeasible to record, entries are selected visually for uniformity, disease resistance, and other agronomic characteristics. We evaluated early generation material in the irrigated bread wheat breeding program at CIMMYT to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield.To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles.Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and more predictive of grain yield than univariate genomic selection.Multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall.This study has been completed and published in Crop Science (see products)
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Gongora-Canul, C., J. D. Salgado, D. Singh, A. P. Cruz, L. Cotrozzi, J. Couture, M. G. Rivadeneira, G. Cruppe, B. Valent, T. Todd, J. Poland and C. D. Cruz (2020) Temporal dynamics of wheat blast epidemics and disease measurements using multispectral imagery. Phytopathology 110(2): 393-405. DOI: 10.1094/PHYTO-08-19-0297-R
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Krause, MR., S. Mondal, J. Crossa, R.P. Singh, F. Pinto, A. Haghighattalab, S. Shrestha, J. Rutkoski, M.A. Gore, M.E. Sorrells, and J. Poland (2020) Aerial high-throughput phenotyping enables indirect selection for grain yield at the early generation, seed-limited stages in breeding programs. Crop Science. 2020(60): 3096-3114. https://doi.org/10.1002/csc2.20259
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Reynolds, M., S. Chapman, L. Crespo-Herrera, G. Molero, S. Mondal, D. N. L. Pequeno, F. Pinto, F. J. Pinera-Chavez, J. Poland, C. Rivera-Amado, C. Saint Pierre and S. Sukumaran (2020) Breeder friendly phenotyping. Plant Science: 110396. DOI: https://doi.org/10.1016/j.plantsci.2019.110396
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Wang X., P. Silva, N. Bello, D. Singh, B. Evers, S. Mondal, F. Pinto, R.P. Singh, and J. Poland (2020) Improved accuracy of high-throughput phenotyping from Unmanned Aerial Systems by extracting traits directly from orthorectified images. Frontiers in plant science 11: 1616. https://doi.org/10.3389/fpls.2020.587093
- Type:
Theses/Dissertations
Status:
Awaiting Publication
Year Published:
2020
Citation:
Megan Calvert, Byron Evers, Xu Wang, Allan Fritz, Jesse Poland (2020) Breeding Program Optimization for Genomic Selection in Winter Wheat bioRxiv 2020.10.07.330415; doi: https://doi.org/10.1101/2020.10.07.330415
|
Progress 11/15/18 to 11/14/19
Outputs
Target Audience:Target audiences: The primary target audience for this project are research scientists and plant breeders in the U.S. and internationally. Efforts: We have made presentations at international meetings, workshops, and publication of scientific papers in peer-review journals. Changes/Problems:We have faced roadblock to implementing UAV data collection in Mexico over the past year. The federal government has implemented new restrictions for operating UAV that have been intractable to navigate in the time period. Therefore, we were unable to obtain needed clearance for UAV flight operations. In place, we have transitioned to manned aircraft flight using the same hyperspectral and multi-spectral imaging. These data are consistent with previous observations, with some reduced ground resolution. However, the datasets are sufficient for plot-level assessment. What opportunities for training and professional development has the project provided?Through the current project year, we have contributed to the training and professional development of two graduate students and two postdoctoral research associates. Each of these students and postdocs have attended multiple research conferences and workshops, given poster and oral presentations and developed professional networks. - Xu Wang, Jesse Poland (2019)Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems.Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV. International Society for Optics and Photonics,Baltimore, MD5/14/2019 - Xuebin Yang,Jesse Polan,Xu Wang (2019)Comparison of single bands, partial spectrum and full spectrum in identifying high-yielding wheat genotypes,American Geophysical Union 2019 Fall Meeting,San Francisco, CA 12/13/19 - Byron Evers, Xu Wang, Paula Silva, Allan Fritz and Jesse Poland (2019)High-Throughput Phenotyping Applications in Kansas Wheat Breeding,2019 ASA-CSSA-SSSA International Annual Meeting,San Antonio, Texas,11/12/2019 ----- How have the results been disseminated to communities of interest?Training Opportunities We provided a field presentation to the KSU Plant Pathology REEU (Research Experience for Undergraduates). We demonstrated field-based high-throughput phenotyping using UAS to about 15 undergrad students. Following that, we provided a training session to the undergrad students and five more grad students and post-docs from KSU Plant Pathology, instructing them with step-by-step procedures on how to process the UAV images and extract plot-level trait from the images. - Automated processing pipeline for multi-spectral imaging We have developed the automated pipeline for pre-processing UAV image data from the MicaSense RedEdge(-M) camera, including renaming, geospatial grouping, and radiometric calibration.The automated pipeline generates geo-rectified orthomosaic photos of wheat breeding field from UAV image data, including identification of ground control points for camera position optimization and photogrammetry processing UAV images.The image processing is followed by automated pipeline for extraction plot-level traits (e.g. vegetation indices and canopy height) from orthomosaic photos of wheat breeding field.The pipeline of extracting plot-level trait consists of (1) cropping single-plot images from an orthomosaic of the complete field, (2) converting pixel values to trait values through raster calculation, and (3) summarizing the plot-level trait in each plot-level image.The trait extraction procedure was implemented using Python, and the source code is available online (github.com/xwangksu/traitExtraction). Through further study, we found the plot-level orthomosaic image generated by the photogrammetry process might fail to reflect trait variation that seemed apparent between plot-level raw images, leading to potential loss of information.This lost information during the photogrammetry process could be partially recovered, whereas simply blending the information could undermine the quality of phenotypic data. With that, we refined our processing pipeline to export multiple orthorectified raw UAV images rather than the blended orthomosic image.The complete pipeline was implemented using Python, and the source code is available online (github.com/xwangksu/bip).Following that, multiple plot-level trait observations can be extracted from the orthorectified images rather than only one plot-level trait extracted from the orthomosaic image.Through comparing the plot-level trait extracted from the orthomosaic and orthorectified images, we found strong evidence that the trait collected by UAS remote sensing could be better estimated by extracting observations directly from multiple orthorectified images and using proper models. ------ What do you plan to do during the next reporting period to accomplish the goals?Complete assess optimal selection strategies for breeding programs that are implementing HTP and genomic profiling through simulation of potential genetic gain from all possible combinations of resource allocation pilot study comparing small plot yield prediction with visual selections to assess selection gain and practical utility cost-benefit analysis of the UAV high-throughput phenotyping pipeline relative to current selection methods
Impacts
What was accomplished under these goals?
Progress on specific goals and objectives: 1. Develop and deploy robust and scalable unmanned aerial systems with a range of spectral and thermal imaging capabilities; including standardized protocols for routine collection of field-based HTP measurements using UAVs over large wheat breeding nurseries - 1.1.Deployment of UAVs in KSU and CIMMYT breeding programs -DJI Matrice 100 UAVs equipped with multispectral sensors were used at 5 locations across Kansas in the K-State wheat breeding program and in the CIMMYT wheat breeding program at Obregon, Mexico, and at three locations in India. Missions were completed throughout the growing season to collect data during critical physiological growth stages.During the project year, we evaluated over 20 location / experiments with UAS with 5 to 10 observation dates for all locations with up to 46 flights for targeted locations.This dataset for 2019 again covers over 100,000+ plot level observations for full yield plots. - 1.2.High-resolution imaging from UAV -To generate extremely high resolution (e.g. sub-mm pixel resolution) from a UAV, we completed testing and deployment of the DJI M600 with DJI Zenmuse X5R4K video camera recording RAW image frames at 24p.The M600 with integrated differential correction GPS for precise positioning can be navigated directly over the plots at low altitude of 5m for extremely high-resolution imaging.Using the X5R video imaging, we completed flights at 21 time points from early May to mid-June in Kansas for a winter wheat association mapping panel of 1,568 plots to generate image dataset for scoring heading date and awned/awnless. Using this low-altitude remote sensing UAS system, we captured big sets of super high-resolution images of wheat canopy through multiple crop growth stages.To identify individual plot from aerial images robustly and automatically, we developed an image processing pipeline.The main steps of image processing included 1) image conversion, 2) image augmentation, 3) image scoring, and 4) plot identification.Preliminary results indicate the methods can highly accelerate the process of linking genotypes to individual plot images and can be fully automated.The whole image processing pipeline was implemented using Python and the source code was available online (github.com/CameronAmos/Plot-Identification-Pipeline).The developed pipeline has now been published (see products) - 1.3.Hyperspectral imaging from UAV -We continue with initial testing with Headwall Nano Hyperspectral camera (400 - 1000 nm) mounted on DJI M600 UAV.Pilots and technicians in the KSU group have completed additional training for operation. We collected hyperspectral data of the winter wheat experiment fields in both Manhattan and Colby, Kansas, with Headwall Nano Hyperspectral camera (400 - 1000 nm) mounted on DJI M600 UAV.This Nano camera collects reflectance data at spectral 270 bands (bandwidth 2.2 nm), evenly distributed in the visible and near-infrared region of 400 to 1000 nanometer.Three sets of hyperspectral data were taken for each experiment field from middle to late June, during the grain filling stage of the wheat.The UAV flew at 100 m above ground at the speed of 2 m/s, with a field of view (FOV) of 13.6 m.The frame rate of this push-broom camera was set at 100 Hz.The collected raw imagery has a spatial resolution of 2.5 cm. - 1.4.Thermal imaging from UAV -We have tested and obtained initial thermal image sets of spring wheat nurseries using the DJI M100 UAV with a FLIR Vue Pro R thermal camera.We have generated complete datasets for initial testing of thermal imaging for processing and plot-level data extraction.We developed a thermal image processing pipeline, mainly including 1) preprocessing, 2) photogrammetry processing, and 3) converting pixel values to temperature.We observed canopy temperature extracted from the orthomosaic image seem to be blended, as large differences were clearly noticeable between plot-level temperature values in the orthomosaic image and the raw image.After that, we refined the thermal image processing pipeline through exporting orthorectified raw images and extracting multiple temperature values per plot.The result indicated that extracting plot-level temperature values from multiple orthorectified aerial images yielded increased estimates of heritability with proper modeling. ----- 2.Complete foundational work to develop and validate integrated genomic and physiological models for yield prediction; including novel statistical modeling of hyperspectral data Using data from previous manned aircraft flights, we have tested multiple modeling approaches for incorporating spectral measurements to genomic selection models.We have developed and validated an approach to measure lodging in plots from Digital Elevation Models (DEMs) derived from UAV imaging.We have validated these UAV derived lodging measurements and the impact of the measured lodging on yield.A manuscript detailing this approach and findings has been submitted and is under review. - 2.1.Estimation plant height and modeling of in-season crop growth -Optimization of morphological and developmental characteristics is critical to maximize the yield potential in crop varieties.Plant height is a key agronomic trait that is an important breeding target in many crops due to its association with biomass and grain yield.However, the slow and static nature of the conventional measurements is a major bottleneck to elucidate the genetic basis of the temporal dynamics of plant height in field experiments.We used the UAS estimated canopy height measurements and demonstrate power to dissect the genetic and physiological underpinnings of growth components in 546 elite CIMMYT spring wheat breeding lines grown in normal and early planting field experiments in South Asia.Plant height estimates were extracted for 2400 plots at 34 timepoints using digital elevation models from UAS imaging.A logistic regression growth model was fit to derive genotype specific growth parameters, namely: upper asymptote (final height), slope (growth rate), and inflection point (time of maximum growth rate).These growth parameters exhibited very high heritability (0.82-0.93) in the two studied environments.By leveraging these highly heritable phenotypic measurements, we found 35 association signals.Multiple coincident signals around known developmental genes were observed for growth rate, heading and maturity dates, as well as other agronomic traits, suggesting a significant genetic interdependence of morphological and developmental processes in wheat.Significant physiological tradeoffs associated with faster crop growth in two environments were also uncovered.Through integration of dynamic growth parameters with physiology and genomics, our study provides strong evidence in support of large-scale, field-based phenomics to uncover the biological underpinnings of complex plant developmental traits. - 2.2.Hyperspectral prediction modeling -To better utilize the UAV-based hyperspectral imagery in wheat yield estimation, we tested three approaches, namely NDVI, chlorophyll potential and partial least squares regression using the hyperspectral data to predict grain yield.While NDVI uses two single bands, chlorophyll potential uses partial spectrum and partial least squares regression uses the full spectrum.Result suggests that while NDVI explained 19% of the yield variability, chlorophyll potential explained 28% of the variability.The coefficient of determination equals 0.43 between measured yield and estimated yield by partial least squares regression.Their mean absolute errors (t/ha) are 0.73, 0.66 and 0.6 respectively.This result implies that it would be more reliable to identify high-yielding wheat genotypes with the full spectrum than with single bands or partial spectrum. -------
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Juliana, P., O. A. Montesinos-L�pez, J. Crossa, S. Mondal, L. Gonz�lez P�rez, J. Poland, J. Huerta-Espino, L. Crespo-Herrera, V. Govindan, S. Dreisigacker, S. Shrestha, P. P�rez-Rodr�guez, F. Pinto Espinosa and R. P. Singh (2019) Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat. Theoretical and Applied Genetics 132(1): 177-194. DOI: 10.1007/s00122-018-3206-3
- Type:
Journal Articles
Status:
Published
Year Published:
2109
Citation:
Krause, M. R., L. Gonz�lez-P�rez, J. Crossa, P. P�rez-Rodr�guez, O. Montesinos-L�pez, R. P. Singh, S. Dreisigacker, J. Poland, J. Rutkoski, M. Sorrells, M. A. Gore and S. Mondal (2019) Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. G3: Genes|Genomes|Genetics 9(4): 1231. DOI: 10.1534/g3.118.200856
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Singh D., X. Wang, U. Kumar, L. Gao, M. Noor, M. Imtiaz, R. P. Singh, J. Poland (2019) High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat. Frontiers in Plant Science. 10(2019), 394.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Sun, J., J. A. Poland, S. Mondal, J. Crossa, P. Juliana, R. P. Singh, J. E. Rutkoski, J.-L. Jannink, L. Crespo-Herrera, G. Velu, J. Huerta-Espino and M. E. Sorrells (2019) High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage. Theoretical and Applied Genetics 132(6): 1705-1720. DOI: 10.1007/s00122-019-03309-0
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Wang X., H. Xuan, B. Evers, S. Shrestha, R. Pless, J. Poland. (2019) High-Throughput Phenotyping with Deep Learning Gives Insight into the Genetic Architecture of Flowering Time in Wheat. GigaScience. 8:11
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Wang X., H. Xuan, B. Evers, S. Shrestha, R. Pless, J. Poland. Supporting data for High throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat. GigaScience Database. (2019) dx.doi.org/10.5524/100566.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Wang X., C. Amos, M. Lucas, G. Williams, J. Poland. (2019) Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV. 11008(2019), 110080D. International Society for Optics and Photonics
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Yang, X., 2019. Woody plant cover estimation in Texas savanna from MODIS products. Earth Interactions 23, 114.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Yang, X., Crews, K., 2019. Applicability analysis of MODIS tree cover product in Texas savanna. International Journal of Applied Earth Observation and Geoinformation 81, 186194.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Yang, X., Crews, K., Frazier, A.E., Kedron, P., 2020. Appropriate spatial scale for potential woody cover observation in Texas savanna. Landscape Ecology 35, 101112.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Wang, X., Poland, J. (2019). Small plot identification from video streams for high-throughput phenotyping of large breeding populations with unmanned aerial systems. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV. International Society for Optics and Photonics. Baltimore, MD, May 14, 2019.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Yang, X., Poland, J., Wang, X. (2019) Comparison of single bands, partial spectrum and full spectrum in identifying high-yielding wheat genotypes. American Geophysical Union Fall Meeting, San Francisco, CA, December 2019.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2019
Citation:
Evers, B., Silva, P., Wang, X., Fritz. A, & Poland, J. (2019). High-Throughput Phenotyping Application in Kansas Wheat Breeding. 2019 ASA-CSSA-SSSA International Annual Meeting. San Antonio, TX.
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Progress 11/15/17 to 11/14/18
Outputs
Target Audience:Target audiences: The initial target audience for this project are research scientists and plant breeders in the U.S. and internationally. Efforts: We have made presentations at international meetings, workshops, and publication of scientific papers in peer-review journals. Changes/Problems:We have faced initial problems with collecting useable data from the Headwall Nano hyperspectral camera. The data output is very sensitive to tuning the correct camera settings and flight parameters. To date, we have generated several useable datasets, but the quality for plot-level data extraction is not sufficient for further analysis. We are currently working with another group at KSU who is deploying the same camera and with technical reps from Headwall to optimized the flight parameters and camera settings. This is being done currently (July - Sept 2018) in off-season on sorghum field trials. We have faced issues with collecting useable data from FLIR VUE Pro R thermal camera. The pixel values of the same plot shown in continuous images have huge differences. We are currently trying to implement radiometric calibration upon the raw thermal images. This is being done off season on sorghum field trials. What opportunities for training and professional development has the project provided?Through the current project year, we have contributed to the training and professional development of two graduate students and two postdoctoral research associates. Each of these students and postdocs have attended multiple research conferences and workshops, given poster and oral presentations and developed professional networks. Xu Wang Xu Wang, Jesse Poland Improving Genomic Prediction with High-Throughput Phenotyping 2018 Coalition for Advancing Digital Research & Education Oklahoma State University, Stillwater, OK NIFA IWYP 4/18/2018 Xu Wang Xu Wang, Jesse Poland An Automated Image Processing Pipeline for High-Throughput Phenotyping with Unmanned Aerial Systems NIFA FACT Workshop: Big Data Analytics in Plant Breeding and Satellite Data and Modeling Workshop Arlington, Virginia NIFA IWYP 2/26/2018 Daljit Singh D. Singh, V. Tomar, X. Wang, U. Kumar, R. Singh, and J. Poland Phenomics-enabled Genetic and Physiological Dissection of Early Ground Cover in Wheat. National Association of Plant Breeders University of Guelph, Guelph, Canada USAID, NSF 8/9/2018 Daljit Singh Daljit Singh, Jesse Poland Phenomics-enabled genetic dissection of lodging in wheat through unmanned aerial systems. Dupont Plant Breeding Symposium Cornell University, Ithaca USAID, NSF 3/9/2018 Daljit Singh Daljit Singh, Jesse Poland Drone-based high-throughput phenotyping reveals genetic architecture of lodging in wheat. Dupont Plant Breeding Symposium Iowa State University, Ames USAID, NSF 3/2/2018 Atena Haghighattalab UAS Symposium: Applications in Precision Agriculture AAG 2018 New Orleans, LA April 2018 How have the results been disseminated to communities of interest?Continue...... 2.2. Hyperspectral prediction modeling To assess the utility of using aerial hyperspectral reflectance phenotypes within genomic selection to predict wheat grain yield, we deployed a manned aircraft equipped with a hyperspectral camera to phenotype five differentially managed treatments grown in four breeding cycles, totaling 20 site-years. During each flight, canopy reflectance was collected at 62 narrowband wavelengths between 400 and 850 nm. Hyperspectral reflectance-derived relationship matrices were calculated by taking the cross product of the reflectance data for all wavelengths. These relationship matrices were used within single-kernel and multi-kernel GBLUP models, the later using markers or pedigrees to estimate the genetic main effects and hyperspectral reflectance to estimate genotype-by-environment interactions, to predict grain yield within and across site-years. Prediction accuracies using the hyperspectral relationship matrices within single-kernel models performed similar to models using genomic markers or pedigrees to model genetic relationships. Overall, multi-kernel models integrating both hyperspectral reflectance and genomic markers or pedigrees increased prediction accuracy, but the increases were marginal or negligible for some site-years. These results show that genetic relationship matrices derived from aerial hyperspectral reflectance phenotypes may be a useful alternative to genomic markers/pedigrees for predicting grain yield within genomic selection, and that the greater accuracies may be achieved by integrating both genomic marker/pedigree information and hyperspectral reflectance phenotypes within multi-kernel prediction models. 3. Implement novel approaches using deep learning on 'breeder-trained' datasets to score developmental and other important agronomic traits 3.1. High-throughput, high-resolution imaging from UAV Using the M600 platform with DJI X5R ultra HD (4K) resolution video camera, we have collected hundreds of thousands of images of wheat plot throughout heading stages for the next phase of deep learning. We have developed image processing pipelines to extract high-resolution plot-level images for individual field plots from 4K video stream. For testing of connecting genotype-to-phenotype for the deep learning from UAV imaging during the 2017-18 wheat field season, we completed high-resolution imaging of winter wheat association mapping panel at 21 timepoints. For initial testing, we examined two datasets for imaging dates at the end of the season (May 31 and June4, 2018) to score awned/awnless. We extracted 8,602 individual image frames covering individual plots from each data sets and extracted over 94,000 images at 512x512 pixels from these frames to train the convolutional neural networks (CNNs). For a first validation we successfully trained the CNN to score awned / awnless phenotype in wheat directly from UAV images achieving 99% accuracy for individual crop images on a test datasets. CNN for percentage heading (0-100%) is in progress. 3.2. Image Embedding We are also exploring novel approaches to understand a more complete set of visual differences captured in the image data. We train a convolutional neural network to map images to a feature space using the criteria that images from the same plot should be closer together than images from different plots. Initial results show that phenotypes like awned/awnless are clustered automatically even though the algorithm was not explicitly given this meta-data. Work is ongoing to understand more completely the potential of this approach.? 4. Develop robust and efficient analysis pipelines that can facilitate community adoption of field-based HTP and enable in-season selection decisions We have focused efforts to develop fully automated pipelines to process new UAV image data directly to plot level measurements extracted from the images. From this, we have improved database performance and transferred all plot level information to geospatial data types including geo-referenced polygons for plots in the CIMMYT breeding program. 4.1. Automated processing pipeline for multi-spectral imaging We have developed the initial fully-automated pipeline for pre-processing UAV image data from the MicaSense RedEdge(-M) camera, including renaming, geospatial grouping, and radiometric calibration. The automated pipeline generates geo-rectified orthomosaic photos of wheat breeding field from UAV image data, including identification of ground control points for camera position optimization and photogrammetry processing UAV images. The image processing is followed by automated pipeline for extraction plot-level traits (e.g. vegetation indices and canopy height) from orthomosaic photos of wheat breeding field. 5. Assess optimal selection strategies for breeding programs that are implementing HTP and genomic profiling through simulation of potential genetic gain from all possible combinations of resource allocation 5.1. Yield prediction of small plots The irrigated bread wheat breeding program at CIMMYT utilizes a "selected bulk" scheme between the F3 and F5 generations in which one spike from each selected plant is harvested and bulked with spikes from the same cross to form the next generation. At the F6 stage, individual spikes are selected and promoted individually to the F7 which are sown as unreplicated double or triple rows within 0.7-1m plots in Ciudad Obregón. As yield measurements on these plots are unreliable and unfeasible to record, entries are selected visually for uniformity, disease resistance, and other agronomic characteristics. In an attempt to improve selection accuracy at the F7 stage, we are developing an aerial high-throughput phenotyping pipeline that would provide the breeder with quantitative canopy reflectance measurements for each genotype. During the 2016-17 and 2017-18 breeding cycles, sets of approximately 1000 genotypes were sown in two field tests: as unreplicated 0.7m plots and as twice-replicated yield trial plots. An unmanned aerial vehicle equipped with a multispectral sensor was flown over both field tests at multiple time points throughout the growing season. In both field tests, the heritability of aerial NDVI exceeded that of grain yield. Correlations between NDVI measurements upon the unreplicated 0.7m plots and grain yield estimates of the twice-replicated yield plots ranged from 0.35-0.43 in 2016-17 and 0.25-0.42 in 2017-18. These preliminary results indicate that canopy reflectance may be a useful selection tool at the F7 stage. What do you plan to do during the next reporting period to accomplish the goals?1. Develop and deploy robust and scalable unmanned aerial systems with a range of spectral and thermal imaging capabilities; including standardized protocols for routine collection of field-based HTP measurements using UAVs over large wheat breeding nurseries We have tested and deployed improved platforms for multispectral and thermal imaging. We will continue deployment of these platforms over field sites in Kansas, Mexico and India during 2017-18 field season. Complete flight coverage of all CIMMYT and KSU wheat breeding program plots at multiple timepoints throughout growing season Pending operational permits, we will extend UAV deployment in Pakistan and Bangladesh We will complete testing of large M600 UAV with combined camera payload of Headwall Hyperspec camera, FLIR thermal camera, Micasense RedEdge, and DJI X3 camera. Generate complete datasets of hyperspectral (400 - 1000nm) for KSU and CIMMYT breeding nurseries Complete video imaging of genetic populations and breeding plots during heading Complete foundational work to develop and validate integrated genomic and physiological models for yield prediction; including novel statistical modeling of hyperspectral data Test combined genotype x environment (GxE) models using spectral data from South Asia and CIMMYT, Mexico field trials Deployment of hyperspectral imaging from UAV (DJI M600 with Headwall Nano hyperspec) to transition from manned aircraft for hyperspectral data collection Test prediction models using UAV generated hyperspectral imaging Implement novel approaches using deep learning on 'breeder-trained' datasets to score developmental and other important agronomic traits Develop automated processing pipeline for plot-level image extraction from UAV video data Train convolutional neural networks using UAV extracted images to score heading percentage in wheat Test feature extraction for image processing to count number of heads Develop imaging embedding approaches for scoring phenotypes Develop robust and efficient analysis pipelines that can facilitate community adoption of field-based HTP and enable in-season selection decisions Develop and test improved processing pipelines for image extraction from video stream Develop the processing pipeline for plot-level extraction of canopy temperature values from UAV images Assess optimal selection strategies for breeding programs that are implementing HTP and genomic profiling through simulation of potential genetic gain from all possible combinations of resource allocation We will initiate simulation modeling of optimized selection strategies for early generation selection from small plots with varying levels of selection accuracy based on UAV derived yield predictions pilot study comparing small plot yield prediction with visual selections to assess selection gain and practical utility cost-benefit analysis of the UAV high-throughput phenotyping pipeline relative to current selection methods
Impacts
What was accomplished under these goals?
Impact: To realize a new level of yield potential, breeding programs must increase the speed of developing new varieties (increase the rate of genetic gain) by evaluating larger populations, making more accurate selections, and decreasing the length of the breeding cycle. Genomic advancements during the past decade have enabled genomic prediction and selection of complex traits on larger number of breeding lines and at early stages in the breeding cycle. At the same time, however, phenotyping of breeding lines under field conditions has seen minimal advancement and is a critical bottleneck for evaluating large populations. Through this project, we are applying novel developments in remote sensing with unmanned aerial vehicles (UAVs) analyzed with machine vision and deep learning to make improved yield predictions directly within breeding programs in the US and internationally at the International Maize and Wheat Research Center (CIMMYT). We have tested and optimized UAV platforms that can be deployed in field crop breeding programs. Using the rich image datasets from the UAVs, we are working to generated in-season yield predictions by combining genomic information with multiple levels of proximal sensing and deep learning on tens of thousands of breeding lines. These optimized selection strategies using the full array of genomic and phenotypic information will be assessed and delivered to breeders. *How have the results been disseminated to communities of interest? To disseminate these advancements beyond the immediate project team across the breeding community, we are developing user-friendly software tools to analyze UAV imagery, along with efficient algorithms to generate breeder-ready predictions for selection. Progress on specific goals and objectives: 1. Develop and deploy robust and scalable unmanned aerial systems with a range of spectral and thermal imaging capabilities; including standardized protocols for routine collection of field-based HTP measurements using UAVs over large wheat breeding nurseries 1.1. Deployment of UAVs in KSU and CIMMYT breeding programs We deployed DJI M100 UAVs with Micasense RedEdge(-M) multi-spectral cameras in the K-State wheat breeding program locations across Kansas, the CIMMYT wheat breeding program at Obregon, Mexico, and at three locations in India. During the project year, we generated over 150,000 plot level observations for full yield plots and over 140,000 observations for small plots from across these locations. 1.2. High-resolution imaging from UAV To generate extremely high resolution (e.g. sub-mm pixel resolution) from a UAV, we completed testing and deployment of the DJI M600 with high-resolution 4K video camera recording RAW image frames at 24p. The M600 with integrated differential correction GPS (RTK-GPS) for precise positioning can be navigated directly over the plots at low altitude of 5m for extremely high resolution imaging. Using the X5R video imaging, we completed flights at 21 time points during the growing season in Kansas for a winter wheat association mapping panel to generate image dataset for scoring heading date and awned/awnless. 1.3. Hyperspectral imaging from UAV We continue with initial testing with Headwall Nano Hyperspectral camera (400 - 1000 nm) mounted on DJI M600 UAV. Pilots and technicians in the KSU group have completed additional training for operation. 1.4. Thermal imaging from UAV We have tested and obtained initial datasets for thermal cameras including DJI ZENMUSE XT and FLIR Vue Pro R. We have generated complete datasets for initial testing of thermal imaging for processing and plot level data extraction. 2. Complete foundational work to develop and validate integrated genomic and physiological models for yield prediction; including novel statistical modeling of hyperspectral data Using data from previous manned aircraft flights, we have tested multiple modeling approaches for incorporating spectral measurements to genomic selection models. We have developed and validated an approach to measure lodging in plots from Digital Elevation Models (DEMs) derived from UAV imaging. We have validated these UAV derived lodging measurements and the impact of the measured lodging on yield. A manuscript detailing this approach and findings has been submitted and is under review. 2.1. Estimation and yield prediction with early season ground cover Assessment of early season ground cover through conventional methods is time-consuming and subjective. We deployed an image-based supervised classification procedure to estimate digital ground cover from multi-time-point UAV imagery. Early season imaging was completed at multiple timepoints across three locations of advanced yield trials in India for the CIMMYT breeding program. A total of 39 multi-spectral datasets were generated and processed through the automated image processing pipeline. A total of 1.1 million unique data points (vegetation indexes and elevation for ~2650 plots at 39 time points ) was processed to explore the genetic and physiological underpinnings of ground cover in wheat. The digital measurements showed considerable variation in ground cover over the course of field season and among field plots. The digital ground cover assessment showed strong genetic correlations with visual estimates of ground cover (r = 0.85 - 0.99) suggesting that both estimates capture the same underlying genetic variation. In 5 out of 7 environments, the digital ground had higher repeatability (broad-sense heritability) than visual ratings. For a component trait for predicting grain yield, we examined the correlations between yield and the visual or digital assessment of ground cover. In 6 out of 7 comparisons, the digital measures had higher correlation to yield than did the visual assessment. We observed a positive effect of increasing thermal times on the relationship of ground cover and grain yield at our testing locations. Overall, these results highlight the superiority of digital ground cover measurements over conventional ground cover measurements. The accurate ground cover estimates will provide an important component trait for improving in-season yield prediction and for estimates of breeding values from multi-variate models.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Belamkar, V., Guttieri, M. J., Hussain, W., Jarqu�n, D., El-Basyoni, I., Poland, J., Lorenz, A. J., & Baenziger, P. S. (2018). Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program. G3 (Bethesda, Md.), g3.200415.2018. https://doi.org/10.1534/g3.118.200415
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Elbasyoni, I., Lorenz, A. J., Guttieri, M., Frels, K., Baenziger, P. S., Poland, J., & Akhunov, E. (2018). A Comparison Between Genotyping-by-sequencing and Array-based Scoring of SNPs for Genomic Prediction Accuracy in Winter Wheat. Plant Science. https://doi.org/10.1016/J.PLANTSCI.2018.02.019
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Juliana, P., Singh, R. P., Singh, P. K., Poland, J. A., Bergstrom, G. C., Huerta?Espino, J., Bhavani, S., Crossa, J., & Sorrells, M. E. (2018). Genome wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes. Theoretical and Applied Genetics, 131, 14051422. https://doi.org/10.1007/s00122-018-3086-6
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Crain, J., Mondal, S., Rutkoski, J., Singh, R. P., & Poland, J. (2018). Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding. The Plant Genome, 11(1), 0. https://doi.org/10.3835/plantgenome2017.05.0043
- Type:
Journal Articles
Status:
Other
Year Published:
2018
Citation:
Crain, J., Reynolds, M., & Poland, J. (2017). Utilizing High-Throughput Phenotypic Data for Improved Phenotypic Selection of Stress-Adaptive Traits in Wheat. Crop Science, 57(2), 648659. https://doi.org/10.2135/cropsci2016.02.0135
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1), 53. https://doi.org/10.1186/s13007-018-0324-5
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Wang, X., Singh, D., Haghighattalab, A., Brown, R., & Poland, J. (2018). An Automated Image Processing Pipeline for High-Throughput Phenotyping with Unmanned Aerial Systems. NIFA FACT Workshop: Big Data Analytics in Plant Breeding and Satellite Data and Modeling Workshop, Arlington, VA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Wang, X., & Poland, J. (2018). Improving Genomic Prediction with High-Throughput PhenotypingNo Title. 2018 Coalition for Advancing Digital Research & Education, Oklahoma State University, Stillwater, OK.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2018
Citation:
Poland J., D. Singh, A. Haghihattalab J. Rutkoski, A. Fritz, X. Wang, J. Crossa, R. Singh (2017) Application of UAVs to Increase Genetic Gain, National Association of Plant Breeders, Davis CA, Aug 2017
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Progress 11/15/16 to 11/14/17
Outputs
Target Audience:The initial target audience for this project are research scientists and plant breeders in the U.S. and internationally.We have made presentations at international meetings, workshops, and publication of scientific papers in peer-review journals. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Through the first year of this project, we have contributed to the training and professional development of two graduate students and two postdoctoral research associates. Each of these students and postdocs have attended multiple research conferences and workshops, given poster and oral presentations and developed professional networks. Presentations: (Presenter Xu Wang) Wang, X. & Poland, J. (March 2017). sUAV phenotyping at Kansas State University. NSF Workshop in Field-based High Throughput Phenotyping. Maricopa, AZ (Presenter Daljit Singh) Singh, D., Wang, X., Kumar, U., McGahee, K., Lucas, M. &Poland, J. (2016). Unmanned Aerial System Based High-Throughput Phenotyping of Wheat Breeding Nurseries.Crop Science Society of America Annual Meeting, Phoenix, AZ (Presenter Daljit Singh)Singh, D., Wang, X., Kumar, U. & Poland, J. (2017)Genetic Analysis of Crop Lodging in CIMMYT Wheat Using UAV Based High-throughput Phenotyping.National Association of Plant Breeders, Davis, CA (Presenter Daljit Singh) Singh, D. (2016).High Throughput Phenomics: Towards rapid wheat cultivar development for South Asia.Board of International Food and Agricultural Development (BIFAD) Annual Public Meeting. Des Moines, Iowa (Presenter AtenaHaghighattalab)Haghighattalab, A. & Poland, J. (2017).Application of Moving Circular Spatial Adjustment (MCSA) to increase the accuracy of Spatial Adjustment of Field Trials. Phenome, 2017. Tuscon, AZ (Presenter Daljit Singh)Singh, D., Kumar, U., Wang, X., Lucas, M., Singh, R., Schinstock, D., & Poland, J. (2016). Phenotypic assessment of CIMMYT wheat varieties using a small Unmanned Aerial System. National Association of Plant Breeders Raleigh, NC. (Presenter Xu Wang) Wang, X., Singh, D., Haghighattalab, A., Mcgahee, K., Schinstock, D., & Poland, J. (2016). Canopy Height and GNDVI Extraction from sUAS Captured Images. Phenotypic Prediction: Image Acquisition and Analysis Workshop at Iowa State University Ames, IA. (Presenter Xu Wang) Wang, X., & Poland, J. (2016). Field-Based High-Throughput Phenotyping in Wheat. Kansas Extension Agricultural Agent Update Meeting Manhattan, KS. (Presenter Xu Wang)Wang, X., Lucas, M., Schinstock, D., & Poland, J. (2016). Development and Implementation of a Small Unmanned Aerial Systems Based Phenotyping Pipeline for Plant Breeding Programs. ASA, CSSA and SSSA International Annual Meetings, Phoenix, AZ. How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?Develop and deploy robust and scalable unmanned aerial systems with a range of spectral and thermal imaging capabilities; including standardized protocols for routine collection of field-based HTP measurements using UAVs over large wheat breeding nurseries We have tested and deployed improved platforms for multispectral and thermal imaging. We will continue deployment of these platforms over field sites in Kansas, Mexico and India during 2017-18 field season. Pending operational permits, we will extend UAV deployment in Pakistan and Bangladesh We will complete testing of large M600 UAV with combined camera payload of Headwall Hyperspec camera, FLIR thermal camera, Micasense RedEdge, and Sony RGB camera. Generate complete datasets of hyperspectral (400 - 1000nm) for breeding nurseries Complete flight coverage of all CIMMYT and KSU wheat breeding program plots at multiple timepoints throughout growing season Complete video imaging of breeding plots during heading Complete foundational work to develop and validate integrated genomic and physiological models for yield prediction; including novel statistical modeling of hyperspectral data Test combined genotype x environment (GxE) models using spectral data from South Asia and CIMMYT, Mexico field trials Implement novel approaches using deep learning on 'breeder-trained' datasets to score developmental and other important agronomic traits Develop processing pipeline for plot-level image extraction from UAV video data Train convolutional neural networks using UAV extracted images to score heading percentage in wheat Test feature extraction for image processing to count number of heads Develop robust and efficient analysis pipelines that can facilitate community adoption of field-based HTP and enable in-season selection decisions Complete and deploy fully automated processing pipeline for plot-level extraction of vegetation index values from raw UAV images Develop and test improved processing pipelines for stitching and geo-rectification of images from video stream Assess optimal selection strategies for breeding programs that are implementing HTP and genomic profiling through simulation of potential genetic gain from all possible combinations of resource allocation We will initiate simulation modeling of optimized selection strategies for early generation selection from small plots with varying levels of selection accuracy based on UAV derived yield predictions
Impacts
What was accomplished under these goals?
Impact To realize a new level of yield potential, breeding programs must increase the speed of developing new varieties (increase the rate of genetic gain) by evaluating larger populations, making more accurate selections, and decreasing the length of the breeding cycle. Genomic advancements during the past decade have enabled genomic prediction and selection of complex traits on larger number of breeding lines and at early stages in the breeding cycle. At the same time, however, phenotyping of breeding lines under field conditions has seen minimal advancement and is a critical bottleneck for evaluating large populations. Through this project we are applying novel developments in remote sensing with unmanned aerial vehicles (UAVs) analyzed with machine vision and deep learning to make improved yield directly within breeding programs in the US and internationally at the International Maize and Wheat Research Center (CIMMYT). We have tested and optimized UAV platforms that can be deployed in field crop breeding programs. Using the rich image datasets from the UAVs, we are working to generated in-season yield predictions by combining genomic information with multiple levels of proximal sensing and deep learning on tens of thousands of breeding lines. These optimized selection strategies using the full array of genomic and phenotypic information will be assessed and delivered to breeders. To disseminate these advancements beyond the immediate project team across the breeding community, we are developing user-friendly software tools to analyze UAV imagery, along with efficient algorithms to generate breeder-ready predictions for selection. Progress on specific goals and objectives: Develop and deploy robust and scalable unmanned aerial systems with a range of spectral and thermal imaging capabilities; including standardized protocols for routine collection of field-based HTP measurements using UAVs over large wheat breeding nurseries We have tested and deployed an improved UAV platform, the DJI M100. We have also completed testing of different cameras including Micasense RedEdge and NIR converted DJI X3. Multiple M100 units with RedEdge cameras were put into service this year, making hundreds of flight missions across the project locations in Kansas, India and Mexico. We have generated a combined 100,000+ plot level observations from across these locations. We have tested a larger UAV platform, the DJI M600, with integrated differential correction GPS (RTK-GPS) for precise positioning, longer flights and heavier cameras. We have obtained and completed initial testing with Headwall Hyperspectral camera (400 - 1000 nm) We have tested and obtained initial datsets for thermal cameras including DJI ZENMUSE XT and FLIR Vue. We have generated completed datasets for thermal imaging for further processing. Complete foundational work to develop and validate integrated genomic and physiological models for yield prediction; including novel statistical modeling of hyperspectral data using data from previous manned aircraft flights, we have tested multiple modeling approaches for incorporating spectral measurements to genomic selection models we have developed and validated an approach to measure lodging in plots from Digital Elevation Models (DEMs) derived from UAV imaging. We have validated these UAV derived lodging measurements and the impact of the measured lodging on yield Implement novel approaches using deep learning on 'breeder-trained' datasets to score developmental and other important agronomic traits Using previously collected image datasets from the ground phenotyping platform, we have trained convolutional neural networks to score awned / awnless and percentage heading (0-100%) in wheat directly from images By applying neural network scoring for percent heading scores to time-series imaging we can identify heading date as the midpoint where a given plot is 50% headed Using the M600 platform with DJI X5R ultra HD (4K) resolution video camera, we have collected hundreds of thousands of images of wheat plot throughout heading stages for the next phase of deep learning Develop robust and efficient analysis pipelines that can facilitate community adoption of field-based HTP and enable in-season selection decisions We have focused effort to develop fully automated pipelines to process new UAV image data directly to plot level measurements extracted from the images We have improved database performance and transferred all plot level information to geospatial data types We have developed initial automated pipeline for pre-processing all UAV image data from RedEdge cameras We have developed initial automated for identification of ground control points, image registration and image stitching to geo-referenced orthomosaic combined with plot coordinate overlay to extract plot level data
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2017
Citation:
Sun, J., J. E. Rutkoski, J. A. Poland, J. Crossa, J.-L. Jannink and M. E. Sorrells (2017) Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield. The Plant Genome 10(2). DOI: 10.3835/plantgenome2016.11.0111
- Type:
Journal Articles
Status:
Published
Year Published:
2017
Citation:
Haghighattalab, A., J. Crain, S. Mondal, J. Rutkoski, R. P. Singh and J. Poland (2017) Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery.� Crop Science. DOI: 10.2135/cropsci2016.12.1016
- Type:
Journal Articles
Status:
Published
Year Published:
2016
Citation:
Haghighattalab, A., L. Gonz�lez P�rez, S. Mondal, D. Singh, D. Schinstock, J. Rutkoski, I. Ortiz-Monasterio, R. P. Singh, D. Goodin and J. Poland (2016) Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12(1): 1-15. DOI: 10.1186/s13007-016-0134-6
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2016
Citation:
Poland J., D. Singh, A. Haghihattalab J. Rutkoski, A. Fritz, X. Wang, J. Crossa, R. Singh (2017) Application of UAVs to Increase Genetic Gain, National Association of Plant Breeders, Davis CA, Aug 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland J., D. Singh, A. Haghihattalab, A. Fritz, X. Wang, J. Crossa, R. Singh (2017) Accelerating Wheat Breeding with Genomic Selection and High Throughput Phenotyping, National Agriculture Research Council - Pakistan, Islamabad, Pakistan, April 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland J., S. Mondal, J. Crossa, R. Singh (2017) Accelerating Wheat Breeding with Genomic Selection, Wheat Research Institute, Faisalabad, Pakistan, April 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland J., S. Battenfield, J. Rutkoski, A. Fritz, X. Wang, J. Crossa, R. Singh (2017) Accelerating Wheat Breeding with Genomic Selection and High Throughput Phenotyping, University of Maryland, Department of Plant Science, Feb 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland J., A. Haghihattalab, S. Battenfield, J. Rutkoski, A. Fritz, X. Wang, J. Crossa, R. Singh (2017) Increasing Yield Prediction Accuracy for Wheat Breeding Through Combined Genomic Selection and Field-based High Throughput Phenotyping, PHENOME 2017, Tuscon AZ, Feb 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland, J., X. Wang, R. Pless, H. Wang (2017) Application of Artificial Neural Networks and Real Neural Networks to Field-based High Throughput Phenotyping, Plant and Animal Genome - National Plant Genome Initiative Workshop, San Diego CA, Jan 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland J., S. Mondal, A. Haghihattalab, S. Battenfield, J. Rutkoski, A. Fritz, X. Wang, J. Crossa, R. Singh (2017) Genomic Selection and High Throughput Phenotyping for Wheat Improvement, Bangladesh Agriculture Research Institute, Jamalapur Bangladesh, Jan 2017
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2016
Citation:
Poland, J., X. Wang, R. Pless, H. Wang (2017) Deep learning for high-throughput phenotyping of complex trait in wheat. ASA-CSSA International Annual Meetings, Phoenix AZ, Nov 2016
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2017
Citation:
Poland, J. Haghighattalab, A., Wang, X. & Singh, D. (2017) Field-Based High Throughput Phenotyping using Proximal and Remote Sensing Technologies. Nebraska NRIC Predictive Crop Design: Genome-to-Phenome Conference. Lincoln, NE
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2016
Citation:
(Presenter Daljit Singh)�Singh, D., Kumar, U., Wang, X., Lucas, M., Singh, R., Schinstock, D., & Poland, J. (2016). Phenotypic assessment of CIMMYT wheat varieties using a small Unmanned Aerial System. National Association of Plant Breeders Raleigh, NC.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2016
Citation:
(Presenter Xu Wang) Wang, X., Singh, D., Haghighattalab, A., Mcgahee, K., Schinstock, D., & Poland, J. (2016). Canopy Height and GNDVI Extraction from sUAS Captured Images. Phenotypic Prediction: Image Acquisition and Analysis Workshop at Iowa State University Ames, IA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2016
Citation:
(Presenter Xu Wang) Wang, X., & Poland, J. (2016). Field-Based High-Throughput Phenotyping in Wheat. Kansas Extension Agricultural Agent Update Meeting Manhattan, KS.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2016
Citation:
(Presenter Xu Wang)�Wang, X., Lucas, M., Schinstock, D., & Poland, J. (2016). Development and Implementation of a Small Unmanned Aerial Systems Based Phenotyping Pipeline for Plant Breeding Programs. ASA, CSSA and SSSA International Annual Meetings, Phoenix, AZ.
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