There are many scenarios where data can play an important role on a vineyard. While these situations are can be very different, they follow a similar data-usage pattern. This document page aims to document a common data cycle that can be applied to many areas of your vineyards. To see these concepts in practice, check out our many tutorials.
Data Cycle Steps Include:
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Collection
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There are many ways to collect spatial data on your farm:
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Handheld Collector Data: myEV allows data to be collected directly from a mobile device in the field. Any kind of information that can be counted, seen, etc, by a human being, can be collected using a data collector.
Remote Sensors: More and more, services are popping up coming online that give growers access to remotely captured data – usually gathered by satellites in space. This data can come in many forms but is often a raster image.
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 usually means coloring mapped data based on variablesa variable. Each dataset in myEV (as well as farmsthe farm/farm blocks) has a series of settings for establishing how the data is visualized. Visualization is important throughout the following steps as because 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 boundaries 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 on our maps.
Interpolation
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Even with data filtered and trimmed, it can still be hard to see 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 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.
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Translation
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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.