Potato Disease Detection through Leafs: Leveraging Deep Learning Algorithms for Accurate Diagnosis

The classification of potato diseases is significant for a number of reasons. Diseases can severely lower potato crop output and quality. Early disease diagnosis can assist farmers in taking the necessary steps to stop the illness's spread and lessen its effects on crop output and quality. Farmers can save money by using less costly pesticides and other treatments if diseases are detected early. It can also help prevent the loss of entire crops due to disease. The use of pesticides and other chemicals can have negative impacts on the environment, including water and soil pollution. Early detection of diseases can reduce the need for these chemicals, helping to protect the environment. Potatoes are an important staple food crop worldwide. Disease outbreaks can have a significant impact on food security, particularly in developing countries. Early detection of diseases can help prevent crop losses and ensure a stable food supply. This paper is about the implementation of the CNN (Convolutional Neural Network) models that can help in the detection of various potato diseases with the help of potato leaf. By using some of the deep learning models like VGG16 and VGG19 we will predict whether the potato is diseased or not.