Application of deep learning architectures for accurate detection of olive tree flowering phenophase

Abstract

The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)'s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures.