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Now that we understand our goal, it’s time to get out in the field and gather images that will be used to train our model. When it comes to machine learning, the more data we can feed a model, the more accurate it will be. Therefore, we’ll want to capture as many images as we practically can. We will want to simulate capturing our raw images in the same fashion that we imagine our end users capturing images. For our example, we’d want to snap images using our phone from below our vines during their phase of growth that we expect the model to be used in. Make sure you are at the same distance that you imagine your users being. To make your model more robust, consider capturing images in slightly different conditions. Go out when it is sunny, cloudy, morning, mid-day, and evening. Use different phones if you have access. You might even capture video and use a software program to pull frames out of the video.
Remember, you don’t have to use all of your imagery all at once when working on your model. But having more images is generally easy to accomplish and could help you down the road when you’re looking to improve the accuracy of your model.
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