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Robot safety

Safety first.

Automating agricultural processes using autonomous vehicles and robots has a huge potential for reducing manual labor and optimizing yield. However, self-driven vehicles pose a major safety risk. Therefore, the SAFE project investigated technologies for maximizing the safety of both humans and animals, using multiple perception sensors and state-of-the-art object detection algorithms.

A perception platform was developed including color camera, thermal camera, stereo camera, lidar and radar. A variety of datasets were collected, including the popular and publicly available dataset: FieldSAFE.


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Weed mapping

With camera technology and the latest development in artificial intelligence and big data, we can accurately map weeds in the field.

With weed maps, it is possible to address one of the key elements of precision agriculture — to only act where necessary and thus reduce the environmental impact and benefit the farmer’s economy.

The automated mapping of weeds works both for grasses in high-value crops, as well as mono and dicotyledonous weeds in cereal.

Hover the mouse over the image to see the automatic detection of weeds

Tab on the image to see the automatic detection of weeds

Field maps can be generated from analyzed images. These maps tells where weeds grow and where to act.

Litter detection and mapping

The CamAlien camera system was developed for detecting invasive plant species but it can also recognize roadside litter. When installed on vehicles, CamAlien identifies litter during transit, generating maps highlighting concentrated litter areas to optimize cleanup efforts.


Use drones to scan your crops! In the CROPDRONE-project we mount our high-res cameras on long-range drones and scan and map crops automatically.

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Weedr is a mobile application for weed recognition. Weedr builds on the latest technology in artificial intelligence and acts as a modern weed identification key that instantly tells you which weeds have been photographed.

Simply point the phone at the plants you want recognized. In an instant, you get three possible suggestions, arranged according to their probability.

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Weed Beeter

Knowledge of the location of plants is required to control weeds efficiently with a minimum impact. Weed Beeter is a system that can detect weeds in sugar beets and support the development of intelligent weed control applications.

Weed Beeter has several detection and classification systems that can be used depending on the desired weed control strategy. It can:

  • Detect sugar beets and weeds
  • Estimate the plant cover
  • Estimate the plants’ locations
  • Locate the stems of sugar beets.

Detecting plants’ locations and cover can be used to control weeds using means that requires detecting the weeds’ canopy, such as precision sprayers. Detecting the stems makes it possible to do mechanical weeding under the crop leaves, which can be used for controlling weeding robots.

The stems of sugar beets are detected automatically despite lots of occlusion
Image of sugarbeet before detectionImage of sugarbeet that is automatically detected and highlighted in the image
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Mixed crop mapping

Precision agriculture in mixed crops is often conditional on the local species competition in the field. With camera technology and artificial intelligence, the otherwise unfeasible task of monitoring the local species distribution can be automated with excellence and scale.

Demonstrated in the images is the case of legume content prediction in annual forage mixtures of rye grass and clovers. Here, the legume content is a valuable metric for predicting the crop quality as well as optimizing the fertilization strategies.

Images where crops are segmented based on their type and a map that shows the clover distribution in a field