In an earlier post, we explained how Variety, one of the four Vs of Big Data, applies in a specific domain as is the one of Viticulture and how all this connects with Agroknow’s work (a.k.a. our use case pilot) in the context of the BigDataEurope Horizon 2020 project.
Viticulture is one of those domains of Agriculture, and obviously not the only one, where data are present throughout the entire process, from the vineyard where various activities take place to the laboratory where analyses are being done. Different vineyards to set the experiment, different grapevine varieties or even different samples of the same variety but from different locations to study, different equipment to use, different methods and protocols to follow, all these contain more than one type of data and all these constitute variables that can differentiate the research outcomes.
One of the challenges that we wanted to tackle with our use case pilot is to see how difficult it is to gather all this information and at the same time, to find ways in which we can organize and combine all these heterogeneous data produced in order to visualize this information and reach meaningful conclusions.
For example, before doing anything else, a viticulture researcher has to decide and locate which varieties to study for the purpose of an experiment. A usual procedure for our researcher is to visit a vineyard, choose a variety that he/she wishes to study and then select a specific plant-sample of the chosen variety that meets certain criteria of appearance, health etc. This chosen plant has specific geolocation or geotagging so that we can revisit it at any time, and also it comes with a set of data. The researcher will have to: take a picture of this plant, take samples from various parts of the plant for specific measurements (young shoot sample, leaf sample, bunch sample with the corresponding pictures) and analyses (genetic study), and the researcher will have to do this during different time periods throughout the year (depending on the different stages of plant growth) and for a number of consecutive years.
Eventually, our researcher ends up with a great number of different spreadsheets, different pictures and different analyses and measurements that are all related with a vineyard, a variety and a plant-sample. And what happens when our researcher needs to do the same thing for many more varieties and for many more plant-samples? How can big data (analytics, visualizations and other tools) can help organize and combine all this information?
Wouldn’t it be exciting but above all totally useful to be able to access or view all these different and various data related to the sampling process and have them visualized with just a couple of clicks? Let’s wait and see what our use case pilot has to say about this.