A project to train and evaluate different dnn models for plant disease detection problem, tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data and implement segmentation pipeline to avoid miss-classification due to unwanted input

LightLayers: Parameter Efficient Dense and convolutional Layers for Image Classification (PDCAT-PAAP)

Plant disease detection using VGG16 model, which is a pre-trained model that has been trained on a large dataset of images.