Abstract:
Potato is one of the essential foods and root vegetables in the world and is also the staple
food in some different nations. Globally It is the fourth-largest food and now the seventhlargest growing food in Bangladesh. Most of the time, the fungus infects potatoes. Our
farmers' profit, food demand, and food security majorly depend on Identifying proper
diseases and giving solutions. At present, Image processing technology mostly performing
the detection of identifying diseases. So, we used six different architectures of
convolutional neural networks (CNN) in the deep learning field. The architectures are
MobileNet, Xecption, Inception V3, VGG16, ResNet50, VGG19. From these
architectures, one of the most popular CNN architecture is MobileNet that found the best
result in our observation. For this approach, we used the potato image dataset. Here
classified our dataset into three different classes where infected potatoes are two classes
and a fresh potato class. This work can help to detect the disease of potatoes. This system
will make it easier for farmers and researchers to find out potato diseases. It is much easier
to Identify potato diseases in this method than to detect and classify the potato diseases
manually, and it is possible to Identify potato diseases in a short time. This will be helpful
for those who will be working with CNN.
