Agriculture by definition is an activity usually involving a long period of time. If you add the fact of several sensors acquiring data on several locations, you can easily come up with really big data sets.
Just for the sake of the example, place easy numbers:
10 years activity, 100 humidity/temperature sensors, let's say tomato plantation.
You will need check around 35M of data for simple monitoring of the parameters, but if you're willing to go further you will easily be handling datasets of Gs. What is more, is the fact that when you acquire a lot of parameters at high speed you might find patterns and find some hidden information.
As an example I can imagine a sample rate of 24 samples/day.sensor might be enough to check temperature and humidity on a tomato plantation, but a higher rate of sampling could enable you to correlate this data with some other environmental variables.
Definitely I see a lot of application of data mining and of course big data related to agriculture.
Hi,
You may also consider to take a look to the Open Data field. Many sectors are involved in the Open Data movement. Agriculture is one of them. You may find a lot of Open Data portals providing datasets with agriculture information. From different sources, different regions, different countries. Which mean that there is space for data analytics and data mining operations in the field. E.g. comparing data from different countries.
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