Cloud-based architecture for automated weed control
Date Issued
2017-07-06
Author(s)
Abstract
Automated weed control has received an increased
interest from the scientific community in recent years. Even
tough there is fairly large number of available approaches and
even commercially available systems for weed control, several
challenges exist that need to be assessed. Most of the approaches
use automated detection of weed and apply herbicides with
sprayers on the most infested regions of the land. Automated
weed control has proven to reduce the quantity of applied
herbicides, thus reducing the pollution of products, land and
water. Being a part of the precision agriculture paradigm,
automated weed control can be performed only by accessing large
amount of on the field sensory data including images and videos
from unmanned areal and ground vehicles. With the increased
granularity of the regions which is a consequence of the increased
resolutions of the used vision sensors, there is even larger need
of fast and reliable data processing architectures that allow large
volumes of data to be instantly processed. Furthermore, the weed
detection includes computer vision algorithms that have high
time and space complexity and that often depend on parameters
that need to be tuned. By gathering and processing data from
multiple fields, the parameter estimation can be performed with
higher accuracy and greater reliability. In this paper we propose a
cloud based architecture that elevates the automated weed control
by using the possibilities introduced from the cloud to gather
additional aggregated knowledge from the process of automated
weed control and further improve the process of weed control
data processing and parameter estimation. We discuss the main
benefits of the proposed architecture and the challenges that
need to be overcome for it to be introduced to the agricultural
communities.
interest from the scientific community in recent years. Even
tough there is fairly large number of available approaches and
even commercially available systems for weed control, several
challenges exist that need to be assessed. Most of the approaches
use automated detection of weed and apply herbicides with
sprayers on the most infested regions of the land. Automated
weed control has proven to reduce the quantity of applied
herbicides, thus reducing the pollution of products, land and
water. Being a part of the precision agriculture paradigm,
automated weed control can be performed only by accessing large
amount of on the field sensory data including images and videos
from unmanned areal and ground vehicles. With the increased
granularity of the regions which is a consequence of the increased
resolutions of the used vision sensors, there is even larger need
of fast and reliable data processing architectures that allow large
volumes of data to be instantly processed. Furthermore, the weed
detection includes computer vision algorithms that have high
time and space complexity and that often depend on parameters
that need to be tuned. By gathering and processing data from
multiple fields, the parameter estimation can be performed with
higher accuracy and greater reliability. In this paper we propose a
cloud based architecture that elevates the automated weed control
by using the possibilities introduced from the cloud to gather
additional aggregated knowledge from the process of automated
weed control and further improve the process of weed control
data processing and parameter estimation. We discuss the main
benefits of the proposed architecture and the challenges that
need to be overcome for it to be introduced to the agricultural
communities.
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