DOI: 10.5061/dryad.63xsj3v34
title: Deep learning object detection to estimate the nectar sugar mass of flowering vegetation

description: 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.
description: We selected native, weed-rich grassland
 for automated estimation of pollinator resources due to its widespread
 importance in 'relaxed mowing' of urban green spaces by city
 councils (e.g. Scottish Government, 2019), agricultural payment-by-results
 schemes (Chaplin
 <i>et
 al</i>., 2019),
 and pollination research on road verges and unmown meadows (e.g. Phillips
 <i>et
 al.</i> 2020).
 Indeed Jones <i>et
 al</i>. (2021)
 concluded that management changes on improved grassland have the greatest
 potential to increase floral resource availability across the UK.
 An
 approximate altitudinal limit of 500m asl was imposed to separate this
 habitat type from upland vegetation assemblages. We focussed on nectar,
 although the same approach could be applied to detection of other drivers
 such as pollen, larval foodplants or structural
 features.
 All images were taken from vertical or
 near-vertical, encompassing approximately
 1m<sup>2</sup> of untrampled ground area, with no extraneous objects (e.g. quadrat frame, litter), of a maximum vegetation height of 1m, of minimum size 2MB and in reasonable focus. A Canon Powershot G10 (14.7 Megapixels) was used to compile these training data.