Data from: Automating field based floral surveys with machine learning

The abundance and diversity of flowering plant species are important
indicators of pollinator habitat quality, but traditional field-based
surveying techniques are time-intensive. Therefore, they are often biased
due to under-sampling and are difficult to scale. Aerial photography was
collected across ten sites located in and around Rouge National Urban
Park, Toronto, Canada using a consumer-grade drone. A convolutional neural
network (CNN) was trained to semantically segment, or identify and
categorize, pixel clusters which represent flowers in the collected aerial
imagery. Specifically, flowers of the dominant taxa found in the
depauperate fall flowering plant community were surveyed. This included
yellow flowering Solidago spp., white Symphyotrichum ericoides/lanceolatum
and purple Symphyotrichum novae-angliae. The CNN was trained using 930 m2
of manually annotated data, approximately 1% of the mapped landscape. The
trained CNN was tested on 20% of the manually annotated data concealed
during training. In addition, it was externally validated by comparing the
predicted drone-derived floral abundance metrics (i.e., floral area (m2)
and the number of floral segments) to the field-based count of floral
units estimated for thirty-four 4 m2 plots. The CNN returned accurate
multi-classification when evaluated against the testing data. It obtained
a precision score of 0.769, a recall of 0.849 and an F1 score of 0.807.
The automated floral abundance counting yielded estimates that were
strongly correlated with field-based manual counting. In addition, flower
segmentation using the trained CNN was time efficient. On average, it took
roughly the same amount of time to segment the flowers occurring in an
entire drone scene as it took to complete the abundance count of a single
quadrat. However, the training process, particularly manual data
annotation, was the most time-consuming component of the study. Overall,
the analysis provided valuable insights into automated flower
classification and abundance estimation using drone imagery and machine
learning. The results demonstrate that these tools can be used to provide
accurate and scalable estimates of pollinator habitat quality. Further
research should consider diverse wildflower systems to develop the
generalizability of the methods.