Crop Signaling for Automated Weed/Crop Differentiation and Mechanized Weed Control in Vegetable Crops
Hand-weeding is an expensive component in vegetable weed management, but labor availability and costs are critically important factors for growers to remain profitable. Through this project, researchers are developing new weed control technologies to accurately detect, locate, and automatically kill weeds without damage to a crop.
The University of California – Davis team created machine vision technology to map weeds and identify crop plants. Research results show that this technology is very successful at detecting and distinguishing crop plants from weeds by using a foliar spray crop signaling system. This research is part of a collaborative effort with other states. The core technologies being developed through this joint effort may reduce hand-weeding costs by 25 percent, increasing efficiencies and profitability for growers.
NIFA supports the research through the Specialty Crop Research Initiative.
Learn more about this research at NIFA’s Data Gateway.
Want to read about more impacts like this? Check out Fresh from the Field, a weekly bulletin showcasing transformative impacts made by grantees funded by NIFA.