An efficient Edge-based plant disease detection model uses an enriched dataset and deep convolutional neural network

This research focuses on developing a method to optimize the DCNN (Deep Convolutional Neural Network) classification model for plant diseases. We enriched the data by incorporating data from two public datasets, PlantVillage Dataset (PVD) and CroppedPlant Dataset (CPD), and we trained the model using two-step transfer learning.