DenseNet-ResNet-Hybrid: A Novel Hybrid Deep Learning Architecture for Accurate Apple Leaf Disease Detection

The accurate identification of diseases on apple production is an important issue due to the worldwide importance of apple production in contemporary agriculture. Identifying diseases correctly can be challenging and affects food safety and economic loss significantly. To alleviate this, deep learning approaches, and particularly Convolutional Neural Networks (CNN), have been able to provide new and reasonable options in the agricultural field. In this study, there is a hybrid model proposed, called DenseNet-ResNet-Hybrid, which brings together architectures from DenseNet and ResNet, to provide an improvement in the extraction of features together.