2021
sUAS Weed Mapping in Soybeans
Contributor/Checkoff:
Category:
Sustainable Production
Keywords:
AgricultureCrop protectionHerbicide
Lead Principal Investigator:
Scott A. Shearer, The Ohio State University
Co-Principal Investigators:
Project Code:
Contributing Organization (Checkoff):
Institution Funded:
Brief Project Summary:

This project helps growers identify and map herbicide-resistant weed escapes using small UAS as a precursor for targeted eradication. Weed escapes simply refers to weeds that survive weed management practices. Most weed species produce prolific seed. It’s easier to control weed escapes before they build the soil seed bank. The effort includes building a reference library of herbicide-resistant weed escapes that occur in Ohio soybeans, training Convolutional Neural Nets for sematic segmentation of NADIR imagery generated from fixed wing sUAS overflights, using this technology to map weed escapes and developing methodology for real-time classification of images on-board of the sUAS. The project also includes field tests.

Key Benefactors:
farmers, agronomists, extension agents

Information And Results
Project Deliverables

We will construct a database of weed images. A database is the first key element in any Artificial Intelligence (AI) application, which will be used for training CNNs. Given the increased use of AI applications in precision agriculture, development of a database is a key step for continued use of AI tools. The image library of weed escapes collected in Ohio will support weed classification.

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.