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The research aims to evaluate deep learning models for detecting young white pine using UAV imagery.

February 19, 2026Sensors0 citationsOpen Access

Young White Pine Detection Using UAV Imagery and Deep Learning Object Detection Models

Key Points

The research aims to evaluate deep learning models for detecting young white pine using UAV imagery.
Utilized high-resolution three-band RGB and five-band multispectral imagery from UAV flights.
Evaluated 20 deep learning object detection models in ArcGIS Pro software.
Assessed model performance across three size classes of white pine in varying density areas.
The Faster R-CNN model achieved an average precision of 0.88 across both imagery types.
Confusion matrix analysis showed 91% accuracy in medium and 90% in high-density white pine blocks.
Findings support UAV-DL systems as accurate tools for assessing white pine regeneration.

Abstract

This study demonstrates the power of combining unmanned aerial vehicle (UAV) imagery and deep learning (DL) for monitoring forest regeneration, specifically focusing on young white pine (Pinus strobus). Using high-resolution three-band RGB and five-band multispectral orthomosaics derived from UAV flights, 20 DL object-detection models were evaluated within ArcGIS Pro 3.4 software (Esri Inc., Redlands, CA, USA). The models were tested across study sites in St. Lawrence County, NY, to assess performance on three distinct size classes of white pine, each stratified into low, medium, and high density areas. The Faster R-CNN (F-RCNN) model, particularly when trained with image rotation and no augmentation, significantly outperformed others, achieving an average precision of 0.88 across both imagery types. Subsequent confusion matrix analysis yielded 91% and 90% overall accuracy in medium and high-density white pine blocks, respectively. These findings validate the use of UAV-DL systems as an accurate and efficient tool for operational white pine regeneration assessment, reducing the need for labor-intensive fieldwork.

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