Detecting Weed Through Edge Computer Vision to Reduce Environmental Footprint

Detecting Weed Through Edge Computer Vision to Reduce Environmental Footprint

  • The Results
Challenge completed! Results follow soon.

Impact-driven startup Weedbot is developing a laser weeding machinery for farmers that can localize plants, distinguish between crops and weeds and remove weeds with a laser beam. 50 technology changemakers are building machine learning models to facilitate pesticide-free food production. 

The impact

Laser weeding can substantially reduce or even eliminate the need for chemical herbicides, thus reducing soil and groundwater pollution. This would also facilitate pesticide-free food production and reduce the final price for such food, encouraging people to buy organic food and follow a healthy lifestyle.

 

The project goals

The goal is to develop a high-speed plant image recognition neural network with a speed of 12ms per image or faster and recognition precision of 100-110% of crop polygon (which means up to 10% false positives are allowed).

Sample image with annotated carrots: 

ai weed laser

 

Requirements for the AI

  • Input: high framerate video stream from the top of the crop row.
  • Necessary output: real-time high-speed weed segmentation with precision 1-2mm

 

Additional target requirements

  • Time must be not more than 12ms (from capturing the image till putting the converted lines to the User Datagram Protocol (UDP) queue) 
  • The captured image must cover a 200x200mm working area with carrot seedlings as an object to be recognized.
  • The recognition software should recognize all carrots and up to 10% false positive (e.g. weed identified as “crop”) are acceptable.
  • The weed segmentation can be done either with the same AI that detects carrots or by a separate script like PlantCV.

 

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