Apple Flower Detection using Deep Convolutional Networks

TL;DR

This paper introduces a fine-tuned deep convolutional neural network for robust apple flower detection, significantly improving accuracy and generalization over existing methods in challenging environments.

Contribution

The study presents a novel application of transfer learning for flower detection, achieving high accuracy and robustness across diverse datasets and conditions.

Findings

Recall and precision rates above 90% on challenging datasets

Outperforms three state-of-the-art flower detection methods

Demonstrates strong generalization to unseen flower species and conditions

Abstract

To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than $90\%$. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability.