I am training an algorithm to identify weeds within crops using the YOLOv5 algorithm. This algorithm will be used in the future to identify weeds in images collected by unmanned aircraft (drones) after making an orthomosaic images. Using the open-source LabelImg software, I am labeling images for object detection that were collected with both UAV and hand-held digital cameras. Using both platforms, I collected many images of weeds that will need to be identified.
My question is this: Does it make sense to collect training samples from the hand-held digital camera, since it will be of much higher resolution than the UAV imagery (and thus not used for future imagery collections after the model is trained)? My initial thought is that it would be best to only use the UAV imagery, since it will be the most similar to what will be collected in the future. However, I do not want to throw out the hand-held digital imagery if it could help in the image classification process.