U.S. flag

An official website of the United States government

Other Access

The information on this page (the dataset metadata) is also available in these formats:

JSON RDF

via the DKAN API

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.

FieldValue
Tags
Modified
2019-08-05
Release Date
2019-01-31
Identifier
8e55a170-d35d-4369-94f5-5b8d8f7f9455
Publisher
Ag Data Commons
License
Contact Name
Tabb, Amy
Contact Email
Public Access Level
Public
Program Code
005:040 - Department of Agriculture - National Research
Bureau Code
005:18 - Agricultural Research Service