Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
StadisticsView Usage Statistics
MetadataShow full item record
Author/sSafonova, Anastasiia; Guirado Hernández, Emilio; Maglinets, Yuriy; Alcaraz Segura, Domingo; Tabik, Siham
Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-...
deep neural networks
ultra-high resolution images