Projects / AI Innovation Challenge

Weed and Crops Detection using Computer Vision on Drone Imagery

Project completed!

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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.

The problem

Persistent herbicides can contaminate the environment and cause harm to non-target organisms and human health. The objective of this project was to develop a solution to minimize the use of herbicides.

The project outcomes

The team built a state-of-the-art model that seamlessly integrates with the Skymaps application. The ML model has the ability to detect and identify various types of weed species based on previous annotations, as well as different crops including corn, cereal, sunflower, etc. Additional features include the option for the user to select the crop to “help” the model and the selection of the most probable/focused weeds to “help” the model.

Moreover, the project team also annotated additional weed samples from the field to enhance the model’s performance.

Weed annotation

Example: weed annotation

Impact of the solution: Less spraying of herbicides 

Weed detection

Source: Skymaps

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Good English

A very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with C/C++, C#, Java, Python, Javascript or similar

Understanding of CV, ML and Deep Learning algorithms

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