There are many scenarios where data can play an important role on a vineyard. While these situations are very different, they follow a similar data-usage pattern. This document aims to document a common data cycle that can be applied to many areas of your vineyards.
Collection
Visualization
Filtering and Trimming
Interpolation
Sampling Validation Points
Translation
Collection
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Once data is in myEV, it can be organized by folder, shared with collaborators, and even edited directly.
Visualization
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Visualization is the process of representing data visually. Within myEV, this means coloring mapped data based on variables. Each dataset in myEV (as well as farms/farm blocks) has a series of settings for establishing how the data is visualized. Visualization is important throughout the following steps as it provides visual feedback as data is being processed.
Filtering and Trimming
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Data collected within biological systems (like vineyards) tends to have noise and extend beyond the geographic boundary that we are interested in learning about. myEV provides simple features that allow for noise to be filtered out and data to be trimmed to areas of interest. By filtering and trimming data, our visualization will become more distinct and we will begin to see trends emerging within the data.
Interpolation
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Even with data filtered and trimmed, it can still be hard to gather broad, useful trends within the vineyard. Interpolation is a form of statistical analysis that smoothes geographic data and makes it much more useful for implementing management strategies on the farm. As a bonus, myEV interpolations are rendered onto common grids which make them useful for comparing regions of your vineyards over time.
Sampling and Validation Points
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With most datasets, it is useful to be able to validate the data by collecting a relatively small number of high-accuracy samples in the field that can then be compared with the dataset to ensure a correlation exists. myEV allows for sample points to be generated and data to be collected at those points.
Translation
Once data has been processed and a variety of sample data collected, we can use the myEV translator plugin to translate correlated datasets into useful viticultural data maps. For instance, an NDVI map might be used in conjunction with a handful of berry count sample points to generate a complete berry count map.