Cloud based Data Acquisition and Annotation Architecture for Weed Control
Date Issued
2018-04
Author(s)
Abstract
In this paper we present a short evaluation of a
cloud based architecture for data acquisition and annotation. We
evaluate the implemented system for annotation and give initial
results on the ability of the system to produce accurate labels
on the data. The used data is consisted of plant field images.
The users partially annotate the data and we use segmentation
algorithms for enriching the annotation of the images. We
compare three different segmentation algorithms used for the
annotation. The results show that Grabcut algorithm is better
than Watershed and nearest-neighbor approaches, but there is
still room for improvement.
cloud based architecture for data acquisition and annotation. We
evaluate the implemented system for annotation and give initial
results on the ability of the system to produce accurate labels
on the data. The used data is consisted of plant field images.
The users partially annotate the data and we use segmentation
algorithms for enriching the annotation of the images. We
compare three different segmentation algorithms used for the
annotation. The results show that Grabcut algorithm is better
than Watershed and nearest-neighbor approaches, but there is
still room for improvement.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
2018_weed_detection.pdf
Size
928.9 KB
Format
Adobe PDF
Checksum
(MD5):35474baff028172a01e64e73f62d8f91