POTATO DISEASE CLASSIFICATION USING DEEP LEARNING: A CONVOLUTIONAL NEURAL NETWORK APPROACH

Abstract

The quantity and quality of potato yields are significantly impacted by two types of disease, the early blight, and the late blight, and these diseases are difficult to interpret, time-consuming, and inconvenient. Fortunately, the easy way to detect the disease is through leaf appearance using appropriate models in potato plants. Production of potatoes will substantially rise if the infections are detected early to infect any potato plants. With the help of different models of computer vision and deep learning techniques were used earlier to classify potato diseases from potato leaf images, to produce healthy foods, and to decrease the loss of yields. CNN is the most effective for the classification of potato leaf disease with high accuracy and low loss rate, the faster-converging rate achieved by the deep learning models.