Multispecies fruit flower detection using a refined semantic segmentation network

TL;DR

This paper introduces a robust, end-to-end deep learning approach for multi-species flower detection in orchards, improving accuracy and applicability over traditional methods for crop management.

Contribution

It presents a refined semantic segmentation network trained on a single dataset, capable of accurately detecting various flower species in uncontrolled environments.

Findings

Effective detection across apple, peach, and pear flowers

Robust performance without dataset-specific pre-processing

Improved segmentation detail through refinement

Abstract

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This work proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any pre-processing or dataset-specific training, experimental results on images of apple, peach and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method.