Food and nutritional security will be a grand challenge in the coming decades. The global population is expected to increase to over 9 billion and food demand will grow by more than 50%. Currently, there are 2 billion people worldwide living in poverty, mostly relying on subsistence agriculture in developing countries. While poverty and food insecurity is a complex issue, the development of improved climate-resilient, high yielding and nutritious plant varieties is a critical part of improving food security, increasing income and economic welfare. To address this challenge, innovative approaches are needed to speed up the development of improved plant varieties. In plant breeding and genetics, precise measurements of plant characteristics are needed to accurately determine the effect of important genes and to identify and select the most promising candidate plant varieties. There has been limited technology development in this area, particularly for traits measured in field trials where most measurements are still taken and recorded by hand. This project will develop mobile applications (apps) for measuring plant traits that can be deployed on inexpensive and readily available mobile devices. Initial testing and deployment through collaboration with cassava and wheat breeders will enable rapid dissemination and broad usability. Middle-school and high-school students will also be engaged to test and use the apps to explore plant growth and measure plant traits. Equipping thousands of plant breeders around the world with tools for rapid measurement and analysis of important plant traits will provide the foundation for accelerated development of improved plant varieties that will ultimately result in increased productivity, food security, nutrition and income of smallholder farmers and their families in developing countries.

Over the past decade, the availability of genomic data has exploded while the methods to collect phenotypes have made minimal advancements. This has led to a dramatic imbalance in data sets connecting genotype to phenotype and highlights phenotyping as the remaining major bottleneck in plant breeding programs. This project will advance the field of 3D graphics and modeling, data mining and deep learning through integration of simultaneous ground truth phenotypic measurements and imaging with mobile technology. Building on the success of Field Book (www.wheatgenetics.org/field-book), user-friendly mobile apps for field-based high-throughput phenotyping (HTP) will be developed and deployed. This project will converge novel advances in image processing and machine vision to deliver mobile apps through established breeder networks. Novel image analysis algorithms will be developed to model and extract plant phenotypes. A robust development pipeline will be assisted by 1) real-time field testing through breeding collaborators around the world and 2) middle-school and high-school students using the apps to explore plant growth and quantitative differences under genetic control. To ensure both immediate, broad deployment and functionality on a diverse set of crops, breeder networks for cassava and wheat will be engaged, providing a diverse set of target plant phenotypes, environments, breeding programs and working cultures. By combining data from research programs with ground truth breeder knowledge, this project will lay the foundation for collecting training sets that can subsequently be used to extract and quantify complex phenotypes using deep learning. Open-source apps for smartphones and tablets will consist of both software and documentation so that users will be able to understand how to use the apps. Apps will be distributed through online app stores (Windows Store, iTunes App Store, Google Play), through project websites, and via collaborative plant breeding networks. The resulting source code will be hosted in a public GitHub repository with a GNU General Public License (GPL) open-source license.

Agency
National Science Foundation (NSF)
Institute
Division of Integrative Organismal Systems (IOS)
Application #
1543958
Program Officer
Diane Jofuku Okamuro
Project Start
Project End
Budget Start
2016-08-01
Budget End
2019-07-31
Support Year
Fiscal Year
2015
Total Cost
$542,368
Indirect Cost
Name
Kansas State University
Department
Type
DUNS #
City
Manhattan
State
KS
Country
United States
Zip Code
66506