Paper Title
Disease Detection in Potato Leaf

Potatoes have become the world's fourth most consumed staple food and their global demand is on the rise, largely due to the pandemic. However, diseases affecting potato plants, particularly early blight and late blight, are a major cause of reduced harvest quantity and quality. It is time-consuming and inconvenient to manually interpret these leaf diseases, but fortunately, such diseases can be identified by examining the leaves. As a result, in this research, we present a system that uses deep learning to reliably diagnose two types of potato plant illnesses based on leaf conditions, utilising the convolutional neural network (CNN) architectural models VGG16, VGG19, and RESNET50. With agriculture science advancements and the application relating to artificial intelligence in identifying plant aliments, it is critical to perform relevant analysis of ensure sustainable agricultural growth. Developing a deep learning project for agriculture will involve creating a rudimentary convolutional neural network architecture is used in an easy image classification model that can characterize potato leaf diseases. Keywords - Deep learning, CNN, VGG16, VGG19, RESNET-50