Home  /  Cases  /  Mixed crop mapping

01.3 · Agriculture

Mixed crop mapping

Grass, clover and weeds separated pixel by pixel in a dense canopy, so legume content can be measured instead of estimated.


Agriculture

Semantic segmentation

Biomass

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.

Grass and clover canopy, raw image
The same canopy with each species segmented per pixel
Canopy Species classification
Grass / clover / weed segmentationgsd 4–8 px per mm
Field composition estimated across a plot trial
Field compositionFull-field scan and mapping of clover ratio

More work

See the other cases, or read about the systems behind them.

Ready to talk?

Do you have a big idea
we can help with?