The challenge

Plant breeders around the world focus on improving crops that will thrive in future fields. To be successful, every breeding program must measure the growth, performance and composition of its crops. Doing so requires a large investment of technology and human capital, which can be out of reach for national programs in developing countries. Breeders are shifting to digital data collection, which requires the adaption of technologies to record and organize observations from the field. Breeders also need to analyze the biochemical composition of the plant to understand and improve various quality traits. The existing technology infrastructure is new, costly, not thoroughly validated and hard to maintain, which makes it inaccessible for many breeding programs.

Our approach

Our phenomics team takes a three-prong strategy: enable digital collection throughout the breeding research program, develop software to analyze that data, and measure nutritional traits for human consumption. We are creating a suite of low-cost tools that are easy to adopt, use and maintain in order to streamline data collection and effectively measure candidate varieties. Additionally, we are developing open-source tools that can predict compound concentrations and determine the most nutritious varieties that target food security goals.

“By being able to more accurately and more efficiently measure candidate crop varieties, you can more effectively select the highest performing one. This has a big impact on crop improvement.”

Jesse PolandPhenomics co-lead

Recent highlights

Prospector

Developing and testing an app interface for near infrared spectroscopy that can be used on multiple crops

Handheld spectrometer

A low-cost solution to train a model and predict plant quality traits with spectral scans

Fourier-Transform Infrared Spectroscopy

Creating universal models to measure low-concentration nutrients such as protein, beta carotene, amino acids and fatty acids

Research from our experts

In progress

  • Developing the use of a low-cost NIR spectrometer to predict compositional phenotypes from grain and foliar tissues of sorghum, as well as on root crops.
  • Fourier-Transform Infrared Spectroscopy (FTIR) technology to predict accurate quantification of total fat, protein, and sulfur-containing amino acid is being adapted for a variety of crops at Centers of Innovation.
  • A gas chromatography-mass spectrometry (GC-MS) to measure individual fatty acids and FTIR models will be used to measure individual essential fatty acids and total fats in the seed without any chemical treatments.
  • Prospector, a mobile app for managing NIRS data, is in its final stages of testing.

Recent accomplishments

  • Integration of Waves tool suite into Breedbase
  • Waves R package was used in an active cassava breeding program, allowing it to be validated for the analysis of spectral data collected from breeding trials
  • Development of new and improved PhenoApps for mobile data collection (e.g. Field Book, Intercross, Prospector, Coordinate)
  • Intercross, a mobile app to manage crossing in breeding programs, has been released on GooglePlay

Resources

The phenomics team

Mike Gore

Phenomics co-lead

Jesse Poland portrait

Jesse Poland

Phenomics co-lead

Dil Thavarajah

Phenomics co-lead

Trevor Rife

Phenomics co-lead

Jenna Hershberger

Ph.D. candidate in plant breeding & genetics (Cornell)

Xiaowei Li

Postdoctoral associate (Cornell)

Ryokei Tanaka

Postdoctoral associate (Cornell)

Amod Udayanga

Ph.D. student, Pulse Nutritional Breeding Program (Clemson)

Nathan Johnson

Ph.D. student, Pulse Nutritional Breeding Program (Clemson)

Chaney Courtney

Computational scientist for plant pathology (Kansas State)