CloverSense & SmartGrass
In this project we aim at monitoring the botanical composition of clover-grass fields to allow for targeted fertilization.
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:
- Capture an RGB-image of the clover-grass canopy and semantically segment the image with machine learning techniques.
- Combine canopy information with auxiliary data, such as past weather conditions and crop height, to estimate the clover fraction and the expected yield.