Manually creating plants images dataset for machine learning plant type classification and leaf segmentation
Well, it all depends on what you plan to accomplish with this dataset:


If you are planning on using drones to identify crops in the wild then you should use a drone to take the images and get images in the wild. (By in the wild I mean a free environment that differs from a lab or something like it)
If you are planning on monitoring plants on a constrained environment using fixed cameras so you should gather images on this conditions.


As Rajat pointed, the camera you choose needs to be able to grab all the feature needed to the identification, maybe machine learning approaches can identify plants with way less features than human eye does but maybe it can not. Either way you should consult with someone with expertise on plants to decide what method you gonna use.

Numbers can't hurt:

Well, they can give you some extra work but the more the merrier. One thing you need to keep in mind is that datasets must be representative enough to enable learning methods to work on the real world scenario. For example, if plan on training a algorithm to classify images token from above there is no reason to train it with images from under the plants. 

If you plan on using deep learning, (i.e. you have the time and resources to train it) you should gather a really large number of images or find a way to augment them with meaningful transformations. One way is to use simulated images, from a computer rendering (that is used in UnityEyes for example) and maybe augment it to be more realistic using GANs.

In short terms, my advice would be to formalize what you want this dataset for, consider what restrictions you can guarantee on the test environment and this should give you a list of what you need to create your training dataset.