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Expectation maximization - as most machine learning methods - learn to make decisions from the training data. So unfortunately they won't work on other types of data.

Your model learns that the RGB color of a healthy leaf is something like 140/160/80 +/-10 (I'm simplifying). On the second image, the color is around 50/80/60. So it won't fit by far on the learned model.

There are two solutions:

• Use the same conditions for learning and predicting. In natural conditions this is quite hard, as conditions change often and the leaves have a great variability.
• Try to find other descriptors for training/prediction that are invariant to the conditions and which discriminate well the healthy and sick part of the leaf. Texture is a good starting point (Haralick descriptors, Gabor filters, tensors, wavelets, etc...)