  The evolution of smaller, faster processors and cheaper digital storage
mechanisms across the last 4-5 decades has vastly increased the opportunity to
integrate intelligent technologies in a wide range of practical environments to
address a broad spectrum of tasks. One exciting application domain for such
technologies is precision agriculture, where the ability to integrate on-board
machine vision with data-driven actuation means that farmers can make decisions
about crop care and harvesting at the level of the individual plant rather than
the whole field. This makes sense both economically and environmentally.
However, the key driver for this capability is fast and robust machine vision
-- typically driven by machine learning (ML) solutions and dependent on
accurate modelling. One critical challenge is that the bulk of ML-based vision
research considers only metrics that evaluate the accuracy of object detection
and do not assess practical factors. This paper introduces three metrics that
highlight different aspects relevant for real-world deployment of precision
weeding and demonstrates their utility through experimental results.
