High-throughput phenotyping of large wheat breeding nurseries using unmanned aerial system, remote sensing and GIS techniques

dc.contributor.authorHaghighattalab, Atena
dc.date.accessioned2016-11-15T16:19:58Z
dc.date.available2016-11-15T16:19:58Z
dc.date.graduationmonthDecember
dc.date.issued2016-12-01
dc.description.abstractWheat breeders are in a race for genetic gain to secure the future nutritional needs of a growing population. Multiple barriers exist in the acceleration of crop improvement. Emerging technologies are reducing these obstacles. Advances in genotyping technologies have significantly decreased the cost of characterizing the genetic make-up of candidate breeding lines. However, this is just part of the equation. Field-based phenotyping informs a breeder’s decision as to which lines move forward in the breeding cycle. This has long been the most expensive and time-consuming, though most critical, aspect of breeding. The grand challenge remains in connecting genetic variants to observed phenotypes followed by predicting phenotypes based on the genetic composition of lines or cultivars. In this context, the current study was undertaken to investigate the utility of UAS in assessment field trials in wheat breeding programs. The major objective was to integrate remotely sensed data with geospatial analysis for high throughput phenotyping of large wheat breeding nurseries. The initial step was to develop and validate a semi-automated high-throughput phenotyping pipeline using a low-cost UAS and NIR camera, image processing, and radiometric calibration to build orthomosaic imagery and 3D models. The relationship between plot-level data (vegetation indices and height) extracted from UAS imagery and manual measurements were examined and found to have a high correlation. Data derived from UAS imagery performed as well as manual measurements while exponentially increasing the amount of data available. The high-resolution, high-temporal HTP data extracted from this pipeline offered the opportunity to develop a within season grain yield prediction model. Due to the variety in genotypes and environmental conditions, breeding trials are inherently spatial in nature and vary non-randomly across the field. This makes geographically weighted regression models a good choice as a geospatial prediction model. Finally, with the addition of georeferenced and spatial data integral in HTP and imagery, we were able to reduce the environmental effect from the data and increase the accuracy of UAS plot-level data. The models developed through this research, when combined with genotyping technologies, increase the volume, accuracy, and reliability of phenotypic data to better inform breeder selections. This increased accuracy with evaluating and predicting grain yield will help breeders to rapidly identify and advance the most promising candidate wheat varieties.
dc.description.advisorDouglas G. Goodin
dc.description.advisorJesse A. Poland
dc.description.advisorKevin P. Price
dc.description.degreeDoctor of Philosophy
dc.description.departmentDepartment of Geography
dc.description.levelDoctoral
dc.description.sponsorshipThis work was done through the International Maize and Wheat Improvement Center (CIMMYT), Mexico and supported by the National Science Foundation under Grant No. (IOS-1238187) and through support provided by Feed the Future through the U.S. Agency for International Development, under the terms of Contract No. AID-OAA-A-13-00051. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or of the U.S. Agency for International Development.
dc.identifier.urihttp://hdl.handle.net/2097/34486
dc.language.isoen_US
dc.publisherKansas State University
dc.rights© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectMoving grid adjustment
dc.subjectGeographically weighted regression
dc.subjectRemote sensing
dc.subjectSpatial adjustment
dc.subjectUnmanned aerial system
dc.subjectYield prediction
dc.titleHigh-throughput phenotyping of large wheat breeding nurseries using unmanned aerial system, remote sensing and GIS techniques
dc.typeDissertation

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