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 conceal..., Orthorectified imagery of study sites were constructed using data from a drone image acquisition program completed in the Rouge National Urban Park, Ontario, Canada during the late summer of 2021. These data represent typical late-season flowering landscapes of remnant habitat patches found in Southern Ontario, Canada.
The major flowering plant groups (i.e., Solidago spp and Symphyotrichum spp) were automatically mapped using the convolutional neural netowork model trained in this study., , # Data from: Automating field based floral surveys with machine learning

[https://doi.org/10.5061/dryad.nvx0k6f1t](https://doi.org/10.5061/dryad.nvx0k6f1t)

This repo contains **(1)** orthorectified drone images of the ten study sites located in Rouge National Urban Park, Ontario, Canada. The imagery was collected at low altitude (7m, 15m, or 30m) in September 2024 with the DJI Phantom 4 Pro V2. **(2)** Floral classification maps predicted by the trained convolutional neural network (CNN) that is described in the paper. The CNN was trained to perform multi-classification of the three flower taxa that dominate the Fall flowering landscape in the region. **(3)** The trained CNN model that performs the multi-classification on input drone imagery. **(4)** Tabulation of plot-level floral surveys that were used to ground the truth of the CNN model.

The code is provided as supporting data for

Sookhan N, Sookhan S, Grewal D, MacIvor JS. 2024. Automating field-based floral surveys with machin...