  Machine-learning techniques, namely deep convolutional neural networks, are
pivotal for image-based identification of biological species in many Citizen
Science platforms. However, the construction of critically sized and sampled
datasets to train the networks and the choice of the network architectures
itself remains little documented and, therefore, does not lend itself to be
easily replicated. In this paper, we develop a streamlined methodology for
building datasets for biological taxa from publicly available research-grade
datasets and for deriving models from these datasets using off-the-shelf deep
convolutional neural networks such as those provided by Google's AutoML Vision
cloud service. Our case study is the Portuguese native flora, anchored in a
high-quality dataset, provided by the Sociedade Portuguesa de Bot\^anica,
scaled up by adding sampled data from iNaturalist, Pl@ntNet, and
Observation.org. We find that with a careful dataset design, off-the-shelf
machine-learning cloud services produce accurate models with relatively little
effort that rival those provided by state-of-the-art citizen science platforms.
The best model we derived, dubbed Floralens, has been integrated into the
public website of Project Biolens, where we gather models for other taxa as
well. The dataset used to train the model and its namesake is publicly
available on Zenodo.
