Deep learning object detection to estimate the nectar sugar mass of flowering vegetation

Floral resources are a key driver of pollinator abundance and diversity,
yet their quantification in the field and laboratory is laborious and
requires specialist skills. Using a dataset of 25000 labelled tags of
fieldwork-realistic quality, a Convolutional Neural Network (Faster R-CNN)
was trained to detect the nectar-producing floral units of 25 taxa in
surveyors’ quadrat images of native, weed-rich grassland in the UK. Floral
unit detection on a test set of 50 model-unseen images of comparable
vegetation returned a precision of 90%, recall of 86% and F1 score (the
harmonic mean of precision and recall) of 88%. Model performance was
consistent across the range of floral abundance in this habitat.
Comparison of the nectar sugar mass estimates made by the CNN and three
human surveyors returned similar means and standard deviations. Over half
of the nectar sugar mass estimates made by the model fell within the
absolute range of those of the human surveyors. The optimal number of
quadrat image samples was determined to be the same for the CNN as for the
average human surveyor. For a standard quadrat sampling protocol of 10–15
replicates, this application of deep learning could cut pollinator-plant
survey time per stand of vegetation from hours to minutes. The CNN is
restricted to a single view of a quadrat, with no scope for manual
examination or specimen collection, though in contrast to human surveyors
its object detection is deterministic and floral unit definition is
standardised. As agri-environment schemes move from prescriptive to
results-based, this approach provides an independent barometer for
grassland management which is usable by both landowner and scheme
administrator. The model can be adapted to visual estimations of other
ecological resources such as winter bird food, floral pollen volume,
insect infestation and tree flowering/fruiting, and by adjustment of
classification threshold may show acceptable taxonomic differentiation for
presence-absence surveys.