Progress 12/01/16 to 11/30/19
Outputs
Target Audience:We discussed the project and initial findings with other scientists working on high throughput phenotyping including plant biologists, plant breeders, and engineers. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?A group of graduate students, undergraduate students, and technicians participated the image acquisition process and worked with engineers. Two graduate students worked on analyzing the data from UAV-HTPP and then coupling the genomics data to identifying genes underlying the image-derived trait. How have the results been disseminated to communities of interest?Some internal presentations were given to futher soliciate inputs from biologists and engineers. Disseration was written and one manuscript is being prepared for final submission. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
The objective of this projectwas to empower potential transformative biological discoveries through an in-depth study of multi-point image acquisition and feature extraction using the small unmanned aircraft system (sUAS) and advanced geospatial analytical methods. We accomplished this objective. We completed multi-point imge acquisition with a UAV-HTPP (Unmanned aerial vehicle-based high-throughput phenotyping platform) and extracted time series NDVI (normalized difference vegetation index) data from multispectral images at 5 time points across the growing season of 1,752 diverse maize accessions. We identified genotypic differences and analyzed the dynamics and developmental trends of NDVI during different maize growth stages. Clustering analysis with time series NDVI classified 1,752 maize accessions into 2 groups possessing distinct NDVI developmental trends. Then the time series NDVI data were used in penalized-splines (P-splines) model to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values observed from individual time points and P-splines estimated NDVI curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS for P-splines fitted NDVI values discovered the dynamic change of SNP effect for the trait associated genetic loci, which may suggest the role of gene-environment interplay in controlling NDVI development. Our results suggest the usefulness of UAV-based remote sensing for genetic dissection of NDVI. Our reserach findings provided answers to several critical biological questions. First, different maize genotype groups exhibit unique NDVI patterns across the growing season. Second, we can identify genetic loci that are controlling the overall NDVI patterns. Third, it is only possible to study the effects of some genetic loci with time-series data that are acquired through high througput phenotyping because they may not be detectable in some fixed time points.
Publications
- Type:
Theses/Dissertations
Status:
Submitted
Year Published:
2019
Citation:
Jinyu Wang, 2019. Pattern discovery for genome-wide base composition evolution and genetic dissection of NDVI with UAV-based remote sensing in crops. Iowa State University
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Wu, Yuye, T. Guo, Q. Mu, J. Wang, Xin. Li, Yun Wu, B. Tian, M.L. Wang, G. Bai, R. Perumal, H.N. Trick, S.R. Bean, I.M. Dweikat, M.R. Tuinstra, G. Morris, T.T. Tesso, J. Yu*, and Xianran Li*. 2019. Allelochemicals targeted to balance competing selections in African agroecosystems. Nature Plants 5:1229-1236.
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Progress 12/01/17 to 11/30/18
Outputs
Target Audience:We discussed the project and initial findings with other scientists working on high throughput phenotyping including plant biologists, plant breeders, and engineers. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?A group ofgraduate students, undergraduate students, and techniciansparticipated the image acquisition process. The night image capture process was a brand new experience. How have the results been disseminated to communities of interest?Some internal presentations were given to futher soliciate inputs from biologists and engineers. What do you plan to do during the next reporting period to accomplish the goals?We plan to wrap up a major publication on genome-wide association studies of traits dervied from high throughput phenotyping. Current analysis indicated the great advantage of high throughput phenotyping in terms of progressively monitoring the genetic effect dynamics underlying the NDVI. We plan to show the relationship between agronomic traits collected through traditional approaches and traits dervied from high throughput phenotyping.
Impacts
What was accomplished under these goals?
Specific Aim 1 is to conduct extensive acquisition of the ultra-high spatial resolution remotely sensed data using sUAS at different solar angles and across different phenological stages. Two genetic mapping populations are leveraged for this project. Both populations were specifically developed to study the plant canopy architecture. The first one is a maize population with 400 doubled haploid lines derived from inbreds with contrasting leaf angle and leaf size. The second is a sorghum population of 250 recombinant inbred lines derived from parental inbreds with contrasting plant height, leaf angle, and photoperiod response. In 2018, we conducted extensive image acquisition using sUAS at 5 different phenological stages and 4 different solar angles at onestage. A new set of equipment was used and data quality is much better than 2017. Image analysis is ongoing. Specific Aim 2 is to extend the phenotype characterization to night time and identify the difference in canopy geometry patterns. We conducted night image acquisition using sUAS. Images are being analyzed. Specific Aim 3 is to develop automatic, accurate, and high throughput field plot boundary delineation algorithms through machine learning. Initial progress was made in leverageing the pixel distribution to define the plot boundary.
Publications
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Progress 12/01/16 to 11/30/17
Outputs
Target Audience:We discussed the project and initial findings with other scientists working on high throughput phenotyping. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?A group of 2 graduate students and 3 undergraduate students participated the image acquisition process. 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?We will repeat the experiments and image acquisition in 2017. We plan to conduct night image acquisition and continue to research in plot boundary delineation.
Impacts
What was accomplished under these goals?
Specific Aim 1 is to conduct extensive acquisition of the ultra-high spatial resolution remotely sensed data using sUAS at different solar angles and across different phenological stages. Twogenetic mapping populations are leveraged for this project. Both populations were specifically developed to study the plant canopy architecture. The first one is a maize population with 400 doubled haploid lines derived from inbreds with contrasting leaf angle and leaf size. The second is a sorghum population of 250 recombinant inbred lines derived from parental inbreds with contrasting plant height, leaf angle, and photoperiod response. In 2017, we conducted extensive image acquisition using sUAS at 5 different phenological stages and 4 different solar angles at each stage. With sUAS flying at a lower altutude to increase the resolution, we found out that extra ground control points (GCPs) need to be placed to obtain a high quality orthomosiac. Specific Aim 2 is to extend the phenotype characterization to night time and identify the difference in canopy geometry patterns. Possibilities to conduct night image acquisition using sUAS were explored and we decided to continue to pursue this in 2018 season. Specific Aim 3 is to develop automatic, accurate, and high throughput field plot boundary delineation algorithms through machine learning. Initial progress was made in leverageing the pixel distribution to define the plot boundary.
Publications
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