CNN based Disease Detection Approach on Potato Leaves

Abstract

Potatoes are a well-known vegetable to all of us. If the other countries are taken into consideration, it can be easily concluded that potatoes are the number one vegetable all over the world, which has been increasingly claimed by many Agricultural departments. Despite the hype, potato leaf disease causes significant damage to the potatoes.. Various types of diseases such as early blight, late blight, septoria blight etc. will attack potato plants and exhibit their syndrome in the leaf of these disorders. The farmer would not face incurring major economic losses if these outbreaks are detected at the primary stage and sufficient action is taken. The proposed model will strongly identify and detect diseases of potato leaf stand on image processing methods in this research paper. Machine Learning includes several algorithms, but the CNN model is used for this research to detect the disease from images of the potato leaf because in CNN is used for image classification & it gives the best result than others. There are 5 algorithms is used for this research they are AlexNet, VggNet, ResNet, LeNet & Sequential model which is our offered one. Normal & disorder-impacted leaf were used for the model provided in order to segregate normal and abnormal aspects of potato leaf. Those kind of photographs are then analyzed through the algorithm provide & the potato plant leaf is labeled as either diseased or normal. This provided model established 97% of great precision.