2021
Seed composition estimation from in-season standing crop and terminal seed yield using non-invasive imaging
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Contributor/Checkoff:
United Soybean Board
Category:
Sustainable Production
Keywords:
(none assigned)
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
Vasit Sagan, St. Louis University
Co-Principal Investigators:
Felix Fritschi, University of Missouri
Project Code:
2120-152-0201
Contributing Organization (Checkoff):
United Soybean Board
$326,499
Institution Funded:
St. Louis University
$326,499
Brief Project Summary:
Unique Keywords:
#technology
Information And Results
Project Summary

Project Objectives

Project Deliverables

Progress Of Work

Updated February 27, 2021:
Given the importance of seed composition for the soybean value chain, we proposed to estimate it based on sensor data and imagery collected in the field (ground- and UAV-based approaches), and post-harvest in the lab. First, we will develop novel machine learning and data fusion algorithms to predict phenotypic traits and estimate seed quality parameters based on in-season, UAV-based sensing modalities and data fusion algorithms. Then, we will establish approaches to estimate seed quality parameters for which accurate non-destructive techniques remain elusive. Finally, we will test the approaches’ robustness for identifying genetic markers for breeding programs. Non-destructive in-season and post-harvest assessment of seed composition will be of great value to stem the decline in seed quality, ensuring U.S. soybeans’ value and competitiveness.

Final Project Results

Benefit To Soybean Farmers

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.