Cassava is a staple crop that is important for food safety in parts of Africa. A key challenge in growing the crop is that it is highly sensitive to diseases. Today, experts primarily diagnose these diseases by moving to different parts of the country while visually assessing the state of health of the crops, which is a cumbersome and erratic process. Nevertheless, state-of-the-art deep transfer learning models that can aid the automated diagnosis of these diseases exist. However, these models cannot be deployed on mobile devices because of the limited memory and computational capacity of these devices and there is not enough network coverage to service them from the cloud. To address this issue, we present knowledge distillation as a technique that can be used to build accurate plant disease classification models that are compatible with the capabilities of mobile devices. We train new Mobile-PDC Plant Decisive Classification models that have the same classification accuracy as state-of-the-art PDC models, but are much smaller in size and fit on mobile devices. Our Mobile-PDC models have the MobileNet structure, which makes them compatible with multiple mobile devices. Our experiments demonstrate that we can compress 91.2% of the original state-of-the-art PDC models without losing accuracy.