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Potatoes are a well-known vegetable to all of us. Potato cultivation has been very popular in India form the last few decades. But potato production is being hampered due to diseases like early blight and late blight which are increasing the cost of production. The aim here is to build an automated and rapid disease detection process to increase potato production and digitize the system. Our main goal is to diagnose potato disease using leaf pictures that we are going to do through CNN algorithm. This paper offers a picture that is processing, and machine learning based automated systems potato leaf diseases will be detected and classified. Image processing is the best option for detecting and analyzing these diseases. In this analysis, picture division is done; more than 2000 pictures of healthy and unhealthy potato's leaf, which are collected from Kaggle, and a few pre-prepared models are utilized for acknowledgment and characterization of healthy and diseased leaves. Among them, the program predicts with an accuracy of 91.41% in testing with 30% test data and 70% train data. Our output has shown that CNN exceeds all existing tasks in potato disease detection.

KEYWORDS: CNN, potato leaf disease dataset