417-29 A Suas Based High Throughput Data Acquisition and Processing Pipeline for Breeding Programs.

Poster Number 627

See more from this Division: C01 Crop Breeding & Genetics
See more from this Session: Crop Breeding and Genetics: III

Wednesday, November 18, 2015
Minneapolis Convention Center, Exhibit Hall BC

Daljit Singh, Interdepertmental Genetics and Department of Plant Pathology, Kansas State University, Manhattan, KS
Abstract:

A sUAS-based high throughput data acquisition and processing pipeline for plant breeding programs

*Daljit Singh1, Atena Haghighattalab2, Kyle McGahee3, Mark Lucas4, Xu Wang4, Nan An5, Stephen Welch5, Dale Schinstock3 and Jesse Poland4

1Interdepartmental Genetics, Kansas State University, Manhattan, KS, USA

2Department of Geography, Kansas State University, Manhattan, KS, USA 3Department of Nuclear and Mechanical Engineering, Kansas State University, Manhattan, KS, USA

4Department of Plant Pathology, Kansas State University, Manhattan, KS, USA 5Department of Agronomy, Kansas State University, Manhattan, KS, USA


Efficient and cost-effective phenotyping and genotyping tools are required to achieve desired genetic gains in breeding programs. Recent advancements in sequencing and informatics technologies have made it easier to genotype and analyze large number of individuals efficiently and cost-effectively. Although relatively slow, high throughput phenotyping (HTP) technologies are also catching momentum. Ease of deployment and cost-effectiveness are key considerations for an ideal HTP platform. Small Unmanned Aerial System (sUAS) is a promising tool to address the existing limitations in current HTP tools. A sUAS fitted with lightweight spectral sensors facilitate analysis of a large number of test plots in a high throughput and non-destructive fashion. A sUAS-based data acquisition protocol and a prototype data management pipeline were developed to enable HTP analysis of large breeding nurseries. The pipeline was tested on preliminary datasets collected from multiple sUAS flights over wheat nurseries in India, Mexico and Kansas, USA in 2014 and 2015. Approximately 100 datasets containing information from visible and near-infrared light spectrum were processed. Vegetation indices extracted from over 50,000 breeding plots are being evaluated to assess its relationship with crop agronomic and physiological characteristics. The current work aims to develop a robust, scalable and easily deployable sUAS workflow to enable efficient phenotyping analysis in an accelerated breeding context.

See more from this Division: C01 Crop Breeding & Genetics
See more from this Session: Crop Breeding and Genetics: III