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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 collection of publicly available datasets and models. 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-grape-flower-clusters/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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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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