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Weed and Feed

The objective of the weed and feed competition, as of May 2019, is to develop an unmanned, autonomous machine that will achieve the following goals:

Teams are scored based on Mechanics, Software, Innovative Solution, and Execution.

Subsystems Overview

Computer Vision

The computer vision team subsystem is responsible for designing and implementing algorithms that will enable the machine to perform vision-related tasks, not including navigation, such as localization of crops and weeds in a field. Members of this team will be working primarily with image data and will gain experience in state-of-the-art image processing techniques like convolutional neural networks.


The mechatronics team deals primarily with the mechanical and electrical components of the agBOT, including mechanical mounts and interfaces, wire harness design and electronic control systems. Members of this team will gain experience in part design and fabrication, using general-purpose computers like raspberry pi to control electro-mechanical systems, among other things.

Autonomous Navigation

Members of this team are responsible for developing the autonomous navigation capacity of the machine, such that the machine knows where it is in space, where it is going, how to get there and what to do if it encounters an obstacle.

Machine Intelligence

The Machine Intelligence subsystem generates useful insights from data (excluding image data) and builds computational models to encapsulate these insights and support in-field decision-making. Members of this group will gain experience in data analysis techniques, working with databases and machine learning.

If you are interested in building your skills in any of these areas, please sign up