In our country there is a huge crop wastage due to not been able to detect disease in crop in early stage. Due to which there is a huge monetary losses to farmers and this need to be cured somehow.
##MOTIVATION OF THIS PROJECT-
Generally while applying CNN models problem is that number of parameters required are huge and it takes a lot to time to predict it with an application.Some models such as ALexNet takes 238M params,VGG-16 has 654M params.
So to cure the problem instead of normal convolution we have used depthwise separable convolution which reduces number of params a lot.
