Towards Unsupervised Weed Scouting for Agricultural Robotics
This addresses the deployability issue in agricultural robotics for weed management, though it is incremental as it builds on existing clustering and deep learning methods.
The paper tackled the problem of automated weed scouting in agriculture, which is limited by the need for prior species knowledge, by developing a clustering approach that uses deep learning features and view tying to cluster cotton plants from grasses without such knowledge, achieving successful results on in-field data.
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.