Precision Agriculture (PA) is playing a major role in
modernizing agriculture. PA usually means some sort of automated data
collection (usually using sensors), often followed by an analysis (usually
referred to as data mining) that rely on the historical data from multiple
sites/farms. This analysis serves two goals: it provides manufacturers with
better insight into their products, and it is also translated into
recommendations. There is increasing need for farms to be as productive as
possible, while also minimizing and mitigating any environmental impact of
operations. Data mining, however, usually implies averages and correlations.
Can these serve the goals stated above? There is a long history to prove the
value of large-scale data mining, but it comes with a challenge. Although
intended to do so, PA does not meet all the data needs of individual farms. There
is a gap between multi-site data mining, on one hand, and local (farm-scale) data
needs, on the other. Farming is much more than large-scale averages and
correlations. Experienced farmers often have a “feel” for their land, which is
difficult to translate into hard data that’s specific to a local site. It is
said that “all politics is local”, and so is farming. Thus, data must be
acquired and recommendations must be made based off of local farm scale data.
Ideally, PA and local data can be coupled and complement each other in order to
produce site-specific recommendations.
The global trends identified from data mining are important
and helpful, but they neither intend nor are capable of addressing all the
farm-scale challenges. This is a
well-known challenge in all large-sample statistics (from studies of human
populations to spatial and environmental data): large-scale trends do not
provide local-scale answers. For example in medicine, large scale pharmaceutical
trials are obviously very important when considering population health, but
they won’t tell you about the response of a particular individual to the proposed
treatment. As a patient, you obviously care about the local-scale response, not
just population averages. Similarly,
fish do not die of large-scale averages.
There is more to farming than a sensor in some soil. Farmers
have their own ideas about what data to collect, where to collect it, and how
to figure out which data is most useful.
This is true in many branches of agriculture. We can find an example for
this challenge in an article by Noel Magnin, an agriculture expert, who commented
on a well-known behavior in vineyards (LinkedIn, February 9, 2016): “Quality in wine grapes is due to some water
constraint during the last steps of fruit maturation associated among other
things with secondary metabolites production. Grape quality leads to
wine quality and the best vintages occurred when water constraint is present
and obviously when the maturation stages just before fruit collection have seen
little to no rain. However, there are
within vineyards locations where fruit quality never reaches a level high
enough to result in high quality wine”. So, one can look at average crop
quality parameters, trying to provide some general fixes, but it is the problem
locations, where the soil becomes saturated at critical times, that would make
the difference between poor, medium, and high quality grapes. You need to know
where the trouble spots are, and large-scale averages won’t tell you that.
Similarly, when managing cattle grazing operations, one can
use some industry-based averages about grazing times and non-grazing intervals,
but it would be more beneficial to modify these averages to reflect local
conditions. Data mining provides good prior knowledge, but that prior knowledge
must be updated by local conditions. This means reconciling between prior
knowledge (the averages) on one hand, and site-specific evidence on the other. To
do that, farmers need to explore the history of their farming operations in
pictures, notes, sensor data, lab data and more, all dated and geo-referenced -
and they should be able to explore that data with ease. This is, of course, not
a new idea. What’s missing, however, are the tools that will allow farmers to
do that.
Let’s take a look at a few examples.
1. Data at your fingertips
What we have here is an example on how data could
be organized in myObservatory, a web-based information management and analysis
systems. The blue areas are hand-drawn shapes representing
parcels/blocks/paddocks of particular interest. Each of these shapes acts as a
container for all related data. This could include pictures, reports, notes,
sensor data, lab reports, etc. This
system provides seamless connectivity between desktop and smartphones. So, for
example, pictures can be taken using smartphones, and these pictures are
automatically uploaded onto the myObservatory platform and automatically linked
to the coordinate where it was taken. Data could be imported from external data
providers for added insight, or data can be fed in by third parties (e.g. from
partnered labs offering soil analysis lab services). Once data has been loaded
or collected, it may be analyzed with statistical analysis and charting tools,
geospatial analysis tools, or shared with selected stakeholders.
All Data are access-controlled. The project
or site admin can assign users an appropriate level of access, anywhere from
public view to adding data only, all the way to read/write privileges for any
or all data and adding new users.
This example (courtesy Peter Traverse, Innisfree Farms,
and myObservatory) shows time lapse photos showing one of the grazing areas in
the farm. Want to know how long your herd grazed here? And how long it took for
recovery to occur? Here it is, ready and available at your fingertips. Want to
add notes? Want to link these images with lab data or with data from your
groundwater wells or rainfall data? You can do it with a click of a button
3. Story Viewer
The third example (courtesy Peter Traverse, Innisfree Farms,
and myObservatory) tracks the movements of the herd. Each of the pictures was taken using a smartphone, which automatically geo-tagged and dated them. Once
within transmission range, the picture is uploaded and stored in the farm’s
database, ready and available for analysis. Notes can be added and auxiliary
files may be attached. You do not need a
full-time photographer to take these pictures. You can have all your staff
taking pictures with their smartphones, and then all these pictures would be
automatically assembled and organized by myObservatory. All these pictures
could be easily accessible and searchable by date and location.
In conclusion, farming is local, and farmers need a platform
that will allow them to collect and explore their data with ease. With Story Viewer, Timeline, and with easy
access to data, you are ready to explore your data and make the decisions most
suitable for your farm. For morre information, visit our website at my-observatory.com
