  We present two large datasets of labelled plant-images that are suited
towards the training of machine learning and computer vision models. The first
dataset encompasses as the day of writing over 1.2 million images of
indoor-grown crops and weeds common to the Canadian Prairies and many US
states. The second dataset consists of over 540,000 images of plants imaged in
farmland. All indoor plant images are labelled by species and we provide rich
etadata on the level of individual images. This comprehensive database allows
to filter the datasets under user-defined specifications such as for example
the crop-type or the age of the plant. Furthermore, the indoor dataset contains
images of plants taken from a wide variety of angles, including profile shots,
top-down shots, and angled perspectives. The images taken from plants in fields
are all from a top-down perspective and contain usually multiple plants per
image. For these images metadata is also available. In this paper we describe
both datasets' characteristics with respect to plant variety, plant age, and
number of images. We further introduce an open-access sample of the
indoor-dataset that contains 1,000 images of each species covered in our
dataset. These, in total 14,000 images, had been selected, such that they form
a representative sample with respect to plant age and ndividual plants per
species. This sample serves as a quick entry point for new users to the
dataset, allowing them to explore the data on a small scale and find the
parameters of data most useful for their application without having to deal
with hundreds of thousands of individual images.
