Skymaps is an agtech startup using remote sensing technologies and advanced image analysis for precision farming. Their mission is to improve practices in agricultural interventions and thus achieve sustainable agricultural production.
In this two-month Omdena Challenge, 50 technology changemakers built a computer vision model to identify weed species as well as crop types.
Persistent herbicides can remain active in the environment for long periods of time, potentially causing soil and water contamination and adverse effects to non-target organisms. Herbicides can cause deleterious effects on organisms and human health, both by their direct and indirect action. In this project, you will build a solution to significantly reduce the usage of herbicides.
The project outcomes
The team developed an advanced model that integrates into the Skymaps application.
Detecting and identifying weeds and crops
The ML model is able to:
Identify weed species based on previous annotations
Identify different crops (corn, cereal, sunflower, etc.)
The user needs to be able to select the crop to “help” the model
The user selects the most probable / focus weeds to “help” the model
In addition, the project team annotated additional weed samples from the field.
Example: weed annotation
Impact of the solution: Less spraying of herbicides
The model output is a Geo-referenced vector file (shapefile) with the detected weed zones (polygons).
Building an annotation tool to classify other features
An additional deliverable of the project is to develop an annotation tool for different samples to classify a variety of features (disease, crop damage, water, etc.).
This challenge has been hosted with our friends at