POTATO LEAF DISEASE CLASSIFICATION USING DEEP LEARNING APPROACH

Abstract

As the fourth most popular staple food in the world, potatoes are among the foods that are extensively consumed. Additionally, the global corona virus pandemic is the leading cause of the sharp rise in potato demand. Moreover, the decrease in the quantity and quality of harvest can be attributed to the prevalence of potato illness. It is crucial to promptly and accurately identify these diseases to prevent further deterioration of plant health. This is where our research comes in. By harnessing the potential of deep learning, we propose a powerful solution that utilizes the VGG16 and VGG19 convolutional neural network models to classify 4 types of potato leaf diseases based on leaf conditions. Not only does this approach provide an accurate classification system, but it also enables early detection of diseases, ensuring the well-being of potato plants and a successful harvest. The average accuracy of 97% in this experiment suggests that the deep neural network technique is feasible.