Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

Abstract

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.

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