  Convolutional neural networks trained using manually generated labels are
commonly used for semantic or instance segmentation. In precision agriculture,
automated flower detection methods use supervised models and post-processing
techniques that may not perform consistently as the appearance of the flowers
and the data acquisition conditions vary. We propose a self-supervised learning
strategy to enhance the sensitivity of segmentation models to different flower
species using automatically generated pseudo-labels. We employ a data
augmentation and refinement approach to improve the accuracy of the model
predictions. The augmented semantic predictions are then converted to panoptic
pseudo-labels to iteratively train a multi-task model. The self-supervised
model predictions can be refined with existing post-processing approaches to
further improve their accuracy. An evaluation on a multi-species fruit tree
flower dataset demonstrates that our method outperforms state-of-the-art models
without computationally expensive post-processing steps, providing a new
baseline for flower detection applications.
