As part of our effort to make computer vision accessible to viticulturists, we trained several flower cluster object detection models. These models can be used to automatically detect and count visible clusters in images.
You may be wondering, ‘why create several models to count grape clusters?’ Good question. With so many varieties, locations, trellis systems, etc. it is hard to accurately represent all variations of clusters in a single model. We also tested model training at night with artificial lights and during the day. We captured images in New York, California, and Washington. As we refine our models, we may add more options to choose from or we may consolidate some of them. Either way, you will have to consider which model will best reflect the conditions that you are capturing images in in your vineyards. Are you collecting at night? Then perhaps our nighttime model is the best fit.
Getting Started
To start off, you’ll need a few things to get started:
Setup a myEV Collector
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. 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.
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 >>