2020
Analyzing Soybean Quality with Automated Image Processing
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Contributor/Checkoff:
Louisiana Soybean and Grain Research and Promotion Board
Category:
Sustainable Production
Keywords:
Data analysisData Management
Parent Project:
This is the first year of this project.
Lead Principal Investigator:
Kevin Hoffseth, Louisiana State University AgCenter
Co-Principal Investigators:
Project Code:
Contributing Organization (Checkoff):
Louisiana Soybean and Grain Research and Promotion Board
$5,000
Institution Funded:
Louisiana State University AgCenter
$5,000
Brief Project Summary:

Soybeans receive a quality grade of 1 through 5 as they enter the supply chain. That grade has implications for contract fulfillment and the price farmers earn for those soybeans – but that rating can be subjective. This research focuses on how digital image processing can provide tools to help grain inspectors grade soybeans more efficiently and consistently. The work harnesses the power of automated computer processing to improve soybean grading. Developing algorithms to tell computers how to assess visual characteristics of soybeans requires many images. This project automates this process, incorporating aspects of the visual reference images the USDA provides for grain inspectors.

Key Benefactors:
farmers, agronomists, Extension agents, grain inspectors

Information And Results
Project Deliverables

Final Project Results

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.