You're correct that the leaked data has to be in the training dataset, sure. Now, for your example - if the crop was a feature used when training, your model i

You're correct that the leaked data has to be in the training dataset, sure. Now, for your example - if the crop was a feature used when training, your model inference input should also require that you input the crop type. When you write your inference pipeline, you will realize that - oh, that feature is not available at inference time. I should retrain my model without the feature. It's not really data leakage - it's incorrect use of features when training a model - that model should never make it to production. So, maybe it's an edge case of data leakage.