In this hackathon track, participants will tackle a fundamental challenge in precision agriculture: distinguishing crops from weeds. Distinguishing crops from weeds is a core target for many companies who make robots or precision sprayers for weed control, making it a crucial area of innovation in agricultural technology.
Objective
Participants will label crops and weeds in provided images, then train a computer vision model to automatically detect and classify these plants. This challenge is designed to be accessible to all participants - no coding is required, and the only tool needed is a web browser.
Task
Annotate the crops and weeds in the provided dataset.
Use the labeled data to train a machine learning model.
Test and refine your model to optimize performance.
You will be provided with a dataset consisting of images containing crops (wheat) and weeds (ryegrass).
Useful link: https://www.superannotate.com/blog/introduction-to-image-annotation
Tools
All work will be done using Roboflow, a platform for computer vision tasks. Roboflow integrates all the processes involved in a computer vision project in one place, from data annotation to deploying a final trained model.
A guide to using Roboflow can be found here: https://precision-sustainable-ag.atlassian.net/wiki/x/A4CYKg The guide also describes steps to set up a free account which grants more training credits.
Evaluation
The final evaluation of a participating team will be based on multiple factors, ranging from model performance to the techniques used during the hackathon. A detailed rubric for the evaluation and process of submission can be found here: https://precision-sustainable-ag.atlassian.net/wiki/x/CIC9Kg