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Data from: Apple flower detection using deep convolutional networks

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 (CNN) 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. This dataset comprises mp4 video sequences illustrating each combination of datasets and methods.

Dataset Info

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FieldValue
Authors
Dias, Philipe A.
(ORCID)
Tabb, Amy
(ORCID)
Medeiros, Henry
(ORCID)
Product Type
Audiovisual
Publisher
Ag Data Commons
Contact Name
Tabb, Amy
Contact Email
Public Access Level
Public
Primary Article

Dias, P. A., Tabb, A., & Medeiros, H. (2018). Apple flower detection using deep convolutional networks. Computers in Industry, 99: 17-28.

Methods Citation

Dias, P. A., Tabb, A., Medeiros, H. (2018). Apple flower detection using deep convolutional networks. Computers in Industry, 99: 17-28.

Cites Other Datasets
License
Funding Source(s)
Agricultural Research Service
Dataset DOI (digital object identifier)
10.15482/USDA.ADC/1503382
Program Code
005:037 - Department of Agriculture - Research and Education
Bureau Code
005:18 - Agricultural Research Service
Modified Date
2019-02-01
Release Date
2019-01-31
Ag Data Commons Keywords: 
  • Plants & Crops
  • Traits
  • Maps & Multimedia
  • Images (non-GIS)
  • Organisms
  • Maps & Multimedia
  • Images (non-GIS)
  • Maps & Multimedia
ISO Topic(s):