  Weed scouting is an important part of modern integrated weed management but
can be time consuming and sparse when performed manually. Automated weed
scouting and weed destruction has typically been performed using classification
systems able to classify a set group of species known a priori. This greatly
limits deployability as classification systems must be retrained for any field
with a different set of weed species present within them. In order to overcome
this limitation, this paper works towards developing a clustering approach to
weed scouting which can be utilized in any field without the need for prior
species knowledge. We demonstrate our system using challenging data collected
in the field from an agricultural robotics platform. We show that considerable
improvements can be made by (i) learning low-dimensional (bottleneck) features
using a deep convolutional neural network to represent plants in general and
(ii) tying views of the same area (plant) together. Deploying this algorithm on
in-field data collected by AgBotII, we are able to successfully cluster cotton
plants from grasses without prior knowledge or training for the specific plants
in the field.
