Olive flowering phenology

The study was carried out in southern Croatia. During April, May and June of 2019, hundreds of images were collected with stationary cameras in an olive orchard. The images were obtained during multiple days and at all times of the day. This means that they were taken in various lighting conditions. The weather conditions included clear, sunny weather to overcast and cloudy weather.Cameras were set at a distance of 40 to 50 cm from the tree canopy. For the purpose of machine learning, six image patches (256 x 256 pixels) were extracted from the central part of each original image. This ensured a dataset which consisted of 1420 images, showing details of the tree canopies during various phenological stages. With the aid of an expert in this field, the images were classified as 0 or 1, depending on whether the start of the flowering phase was observed. Please cite as: Milicevic, M.; Zubrinic, K.; Grbavac, I.; Obradovic, I. Application of Deep Learning Architectures for Accurate Detection of Olive Tree Flowering Phenophase. Remote Sens. 2020, 12, 2120.