Noxious weeds are an increasing problem throughout the United States. Early detection is critical to contain or eradicate an infestation. Ground-based monitoring requires time and money to cover a relatively small area. And data collected from ground-based surveys may only provide a limited amount of information about the extent and spread of any target weed. Using an unmanned-aerial vehicle can provide a more efficient method to map and monitor areas for noxious weeds.

Unmanned-aerial vehicles cover large areas in a relatively short amount of time. They provide good spatial resolution, which helps detect small objects such as weeds. The vehicles use light-weight sensors and four- to six-band multispectral cameras to take images in visible and near-infrared wavelengths of light.

Timing of aerial flights is one of the most critical factors for distinguishing characteristics of noxious weeds from surrounding cover. An Oriental bittersweet survey, for example, is done best when leaves have fallen, snow has covered the ground and fruits are bright red. A photo taken from the vehicle is geo-tagged with a global-positioning-system coordinate, making it easy to locate and treat or remove Oriental bittersweet.

Although aerial surveys offer a more efficient method of mapping infestations, there are many weeds that may not be good candidates for remote sensing. Weeds may not be distinguishable from their surroundings. That makes them difficult to locate in an aerial image. Researchers are working to overcome the obstacle by using multi-spectral cameras that work by imaging different wavelengths of light.

Plants typically reflect a large amount of near-infrared light, which isn’t visible to the human eye but is visible to multi-spectral cameras. The cameras have been used for identifying areas of crop stress. They also can help provide a quantitative measure of plant vigor. The Minnesota Department of Agriculture and the University of Minnesota are working together to develop an effective method to aerially monitor Palmer amaranth using a multi-spectral camera. Visit for more information.

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