# Apple Flower Detection using Deep Convolutional Networks

arXiv: https://arxiv.org/abs/1809.06357 · HTML: https://mlsanity.com/paper/1809.06357/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [A fast accurate fine-grain object detection model based on YOLOv4 deep neural network](https://mlsanity.com/paper/2111.00298/) (2021)
- [Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination](https://mlsanity.com/paper/2304.09351/) (2023)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Local Rose Breeds Detection System Using Transfer Learning Techniques](https://mlsanity.com/paper/2304.03509/) (2023)
- [Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?](https://mlsanity.com/paper/2607.28796/) (2026)

## Abstract (arXiv)

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.