The crop productivity depends on environmental factors or product resources, such as temperature, humidity, labor and electrical costs. However, above all, crop disease is the crucial factor and causes 20-30% reduction of the productivity in case of its infection. Thus, the disease of the crop is the important factor affecting the productivity of the crops. Therefore, the farmer concentrates on the cause of the disease in the crops during its growth, but it is not easy to recognize the disease on the spot. Until now, they just relied on the opinion of the experts or their own experiences when the disease is doubtful. However, it triggers a decrease in productivity as no taking appropriate action and time. In this paper, to address this problem the mechanism, which dynamically analyses the images of the disease, is provided. The analysis result is immediately sent to the farmer required the decision and then feedback from the farmer is reflected to the model. The mechanism performs the diagnosing and predicting of the disease with data set of images using deep learning. Thus, it encourages increasing of the productivity through the fast recognition of disease and the consequent action.