Apple Flower Detection using Deep Convolutional Networks

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.

arXiv comment14 pages

References in corpus (7)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Caffe: Convolutional Architecture for Fast Feature Embedding
• How transferable are features in deep neural networks?
• Deep Learning using Linear Support Vector Machines
• Superpixels: An Evaluation of the State-of-the-Art
• Characterization and performance of the ASIC (CITIROC) front-end of the ASTRI camera
• Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

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• AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards
• Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops
• Automatic segmentation of trees in dynamic outdoor environments
• FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
• Kiwifruit detection in challenging conditions
• DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN
• Local Rose Breeds Detection System Using Transfer Learning Techniques