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, food production must increase by 60 % to be
able to feed the growing population expected to hit 9 billion in 2050. The use of
technology to advance agricultural in particular precision agriculture approaches
are no longer regarded “new” [
3
]. However, within the Australian context, a num-
ber of precision agriculture standards e.g. grid based approaches are not suitable
[
2
]. E.g. the sampling grid size standard in precision agriculture methods is 75 m
while for vineyards, the suggested sampling size is 20 m to obtain high resolution
crop information.
The recent advancements in technologies more specifically the Internet of
Things (IoT) paradigm has paved way to high resolution crop analysis at lesser
costs allowing farmers, biologist and plant scientists to capture real-time data
from the field which in the past was dicult and expensive [
3
]. IoT promotes
the vision of a globally interconnected continuum of devices, objects and thing.
The data produced from IoT devices can greatly aid in making informed real-
time decisions that can significantly impact the yield and quality of the produce.
For example, good pasture growth is the basis of productivity in gazing oper-
ations. It is important to understand the key factors affecting pasture growth
when seeking to maximize productivity. Technologies like IoT, broadband con-
nectivity, cloud-computing and smart personal mobile devices can greatly help
in understanding the factors that affect pasture growth (e.g. pH levels of soils,
rate of Nitrogen depletion) in real-time and share it to the benefit of the commu-
nity. These emerging technologies have the capacity to transform/digitalise our
agricultural practices, markets and the way we produce products. Moreover, by
leveraging upon the increasingly important area of Big Data analytic [
12
]and
mobile sensing [
6
,
7
,
9
] further meaningful insights to data can be discovered.
The IoT paradigm has further fuelled the Big Data revolution more specif-
ically in agriculture domain requiring new methods and approaches to process,
store, discover and retrieve data. Organisations such as John Deere [
1
] are cur-
rently exploring vendor-based precision farming tools and techniques to meet
big data challenges in digital agricultural. A vendor specific approach towards
IoT adoption leads to the development of IoT information architecture silos that
have very little interoperable capabilities. The key is to develop an open archi-
tectures that facilitates open sharing of data securely, promotes data discovery
both within and across domains, provides utility-based metrics for usage of data
and service-oriented. One of the fundamental challenges faced by an average
farmer is the ability to cope with explosion of data. Addressing this challenge
requires the development of tools, techniques and interfaces that will allow farm-
ers and other plant biologist and scientist to access relevant data (e.g. outcomes
of plant experiments) by discovering, fusing and analysing data from heteroge-
neous sources such as precision farming equipments and IoT devices using do-
it-yourself tools. E.g. an intuitive mobile notification service can be configured
and deployed by discovering relevant data required to make a decision on when
to irrigate depending on soil condition, nitrogen in the soil, crop growth and cli-
matic condition. In this paper, we present Open Internet of Things (OpenIoT),
a open source IoT middleware. We discuss a digital agriculture usecase namely
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