ResNet for Leaf-based Disease Classification in Strawberry Plant

In the era of the 21st century, Deep CNN has proven its potential in crop and fruit disease classification and detection. Diseases have a ruinous effect on the quality and gross production of yields, which is related to the world economy. Proper identification of diseases at early stages may save yields from damage. CNN-based disease identification can detect the disease at the actual extent at a low cost with minimum expert manpower and labor. Strawberry is considered a functional food, that has a lot of health benefits for the human body. In this study, pre-trained weight ResNet models ResNet50, ResNet101, and ResNet152 architectures are used via the transfer learning features of CNN. Only the classifier of the models is getting updated during training. The Strawberry leaf images are used in this study from the PlantVillage dataset where both classes are balanced in terms of the number of images in each class. Among the three ResNet architectures, ResNet50 outperforms the other ResNet models achieving 88% classification accuracy during the testing period. The ResNet101 and ResNet152 models show 82% and 80% accuracy during the testing period, respectively.