CloverSense & SmartGrass

In this project we aim at monitoring the botanical composition of clover-grass fields to allow for targeted fertilization.

Background

Targeted fertilization allows for increasing the yield as well as the quality of the harvested clover-grass, but requires reliable knowledge of the clover fraction in the mixed crops.

Based on previous research, the problem of determining the clover fraction of the harvested dry-matter is split into two parts:

  1. Capture an RGB-image of the clover-grass canopy and semantically segment the image with machine learning techniques.
     
  2. Combine canopy information with auxiliary data, such as past weather conditions and crop height, to estimate the clover fraction and the expected yield.

Project partners