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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Agricultural Systems Laboratory » Research » Research Project #442122

Research Project: Field-Scale Testing of Weed Identification and Mapping Tools for Accelerating Integrated Weed Management Research and Adoption - Un of NE

Location: Sustainable Agricultural Systems Laboratory

Project Number: 8042-22000-167-041-A
Project Type: Cooperative Agreement

Start Date: May 15, 2022
End Date: Nov 30, 2022

Objective:
GROW: A National Response to the Herbicide-resistant Weed Epidemic” is focused on developing integrated weed management (IWM) strategies that emphasize prevention through cultural, mechanical, physical, chemical, and biological tactics using precision technologies. The objectives of this agreement are to refine precision agriculture technology (computer vision and artificial intelligence tools), to estimate and map weed density and biomass in U.S. soybean production regions, and build a web-based application that automates data analysis and visualization for both farmers and researchers.

Approach:
The research will be carried out at two scales: in small-plots using a hand-held camera system, and on-farm using tractor-mounted cameras. This work will include drones in years two and three. The research network has developed two systems that are both inexpensive and efficient in collecting weed density and biomass. The first system uses a 3-D reconstruction technique (monocular camera and structure from motion analysis of digital videos) using a hand-held pole-mounted GoPro camera. The second system is a tractor-mounted sensor box consisting of multi-spectral, LiDAR, and ultrasonic sensors. Coupled with the data flow systems already developed by our network, both systems are expected to accelerate efficient data collection throughout the growing season and across locations. A mobile app has also been developed for collecting pertinent meta-data that accompanies the collected video data. These technologies have already been beta-tested at select locations in the US. This project will greatly expand the coverage across important U.S. soybean production regions and calibration for more weed species. Further, a web-based user interface will be developed that automates data analysis, visualization, and recommendation systems.