A lack of sufficient training data, both in terms of variety and quantity, is
often the bottleneck in the development of machine learning (ML) applications
in any domain. For agricultural applications, ML-based models designed to
perform tasks such as autonomous plant classification will typically be coupled
to just one or perhaps a few plant species. As a consequence, each
crop-specific task is very likely to require its own specialized training data,
and the question of how to serve this need for data now often overshadows the
more routine exercise of actually training such models. To tackle this problem,
we have developed an embedded robotic system to automatically generate and
label large datasets of plant images for ML applications in agriculture. The
system can image plants from virtually any angle, thereby ensuring a wide
variety of data; and with an imaging rate of up to one image per second, it can
produce lableled datasets on the scale of thousands to tens of thousands of
images per day. As such, this system offers an important alternative to time-
and cost-intensive methods of manual generation and labeling. Furthermore, the
use of a uniform background made of blue keying fabric enables additional image
processing techniques such as background replacement and plant segmentation. It
also helps in the training process, essentially forcing the model to focus on
the plant features and eliminating random correlations. To demonstrate the
capabilities of our system, we generated a dataset of over 34,000 labeled
images, with which we trained an ML-model to distinguish grasses from
non-grasses in test data from a variety of sources. We now plan to generate
much larger datasets of Canadian crop plants and weeds that will be made
publicly available in the hope of further enabling ML applications in the
agriculture sector.Comment: 35 pages, 8 figures, Preprint submitted to PLoS On