Deep learning-based palm tree detection in unmanned aerial vehicle imagery with Mask R-CNN

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Oil palm is highly valuable in tropical regions like Southeast Asia, including
Indonesia. Therefore, accurate monitoring of oil palm trees is necessary for
operational efficiency and reducing its environmental impact. Geospatial
data, such as orthomosaic imagery from the unmanned aerial vehicle (UAV),
can facilitate this goal. This research aims to integrate UAV data with deep
learning algorithms, specifically Mask region-based convolutional neural
network (R-CNN), to detect oil palm trees in Indonesia. We utilized Resnet-
50 as the backbone and trained the model using data sampled from the
template matching tool in eCognition. Considering factors like cloud
shadows and other features, such as other plants, buildings, and road
segments, we divided the study area into three containing different feature
combinations in each. The Mask R-CNN model achieved an accuracy
exceeding 80%, which is sufficient and makes it suitable for large-scale oil
palm tree detection using high resolution images from UAV.