An Hybrid Feature Extraction and Classification using Xception-RF for Multiclass Disease Classification in Plant Leaves

In today's world, agriculture is one of the most important assets as it affects the economy of the country. Biotic stress caused by fungi, bacteria, and viruses is common in plants; hence plant disease recognition is essential as it could identify diseases at the early stages of infection. Most of the previous plant disease works involve PlantVillage dataset, which is taken in a controlled environment. In this paper, the Banana leaf dataset is used which is combined from two datasets. CNNs are becoming more popular in many forms, such as pre-trained models, and in this research, we employed a pre-trained model in conjunction with an ML classifier to predict plant disease. Pre-trained models such as VGG 16, RESNET, MobileNet, InceptionResNetV2, Inception, and Xception models were deployed for feature extraction. Based on the above models, the proposed Xception-RF model achieved 100% accuracy with no misclassified classes. Using an Xception model with depth-wise convolutions reduces the number of parameters and the cost of implementation. The proposed work merges machine learning and deep learning by extracting features using CNN and then using ML-based RF for classification, which has resulted in an increase in performance in terms of accuracy.