Identification of Disease in Potato Leaves Using Convolutional Neural Network (CNN) Algorithm

Potato crops have many benefits for human life, one of the most useful benefits of potatoes for humans is the carbohydrate content in them and carbohydrates are the main food for humans. The development of potato crop agriculture is very important for the sustainability of human life. There are several obstacles in developing potato farming, including a disease that attacks potato leaves which if left untreated will result in poor production or even crop failure in the future. One of the obstacles in the development of potato plants is the disease on potato leaves, namely early blight caused by the fungus Alternia solani, then late bligt disease caused by Microbe phytopthora infestans de bary. This disease has its respective symptoms so that farmers can take precautions if they see symptoms on potato leaves, but in this preventive step can only be done by experts who have knowledge in the field of diseases in potato plants while the average farmer does not have sufficient knowledge. So, the identification process becomes less accurate and takes a long time. Technology in the field of informatics in the form of digital image processing can be used to solve problems in disease identification in potato leaves, so this research will propose the right method for detecting disease in potato leaves. The identification process in this study uses three types of data in the form of healthy leaves, early blight, and late blight. The method used to identify is deep learning using the Convolutional Neural Network (CNN) architecture. The result of this research is that the 70:30 data division produces better accuracy than the 80:20 data division. The accuracy obtained is 97% on training data and 92% on validation data using 20 batch sizes at 10 epochs.