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Data collectors are used in myEV to collect data using mobile devices in the field. As part of this project, we have built a computer vision integration with Roboflow that lets you leverage any open and available model on the Roboflow Universe, a —a collection of publicly available datasets and models. We make our models available there as well. In this tutorial we will be using ‘nighttime-grape-flower-clusters/1’ which is a nighttime cluster counting model and performed in our trials with the highest levels of accuracy. You can explore our other cluster counting models, leverage any Roboflow Universe models, or even train your own model.
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Credit: The data used to train nighttime‘nighttime-grape-flower-clusters/1 1’ was originally captured and trained by Jonathan Jaramillo as part of his PHD research which motivated the creation of this guide. Read the original paper >> |
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Login to your myEV farm. We recommend doing configuration on a desktop computer, though you can set this up on your phone as well.
Select the ‘Collect Data’ button. Then click ‘New Data Collector’.
Give your collector a name and then select ‘Add a Header’.
Give your new header a label and select ‘Computer Vision’ under the ‘Header Type’ field. Enter your Roboflow API Key and the Model URL as shown in the images below. Our nighttime cluster counting model’s URL is
https://detect.roboflow.com/nighttime-grape-flower-clusters/1.
Once you’ve updated these details, click ‘Add Header’ and then ‘Save’ the data collector. You are now ready to use this data collector to take images and count clusters in the field!
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Note: ‘nighttime-grape-flower-clusters/1’ is a nighttime-only model. This means you’ll need to capture vine images at night using a lighting source. We recommend placing light sources a few feet above and/or below your camera as in the figure below:  Here is an example image that we will be using in this tutorial to count clusters:  |
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Always test and check the accuracy of these tools! We recommend dedicating time to ground-truthing the results of these computer vision tools by manually counting clusters and comparing the model’s inferences vs the actual cluster count. This ratio can be used to make better predictions of clusters in the field. |
Next Steps
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