Deep learning ensemble based on transformer and CNN features for tomato leaf disease classification

Plant disease detection is a one of the most studied subjects in precision agriculture which aims to protect and improve agricultural crops. Commonly, intelligent systems based on CNN (Convolutional Neural Networks) are employed to identify multiple plant diseases by analyzing leaf images. In this work, we propose a novel deep feature fusion to improve tomato disease classification. Precisely, deep features extracted from the Compact convolutional transformer are fused with CNN features generated by using the MobileNetV2 and the DenseNet201. Fused features are trained by a SVM classifier which achieves the classification into multiple disease classes. Experiments conducted on a set of 10 tomato disease categories highlight the effectiveness of the proposed system which outperforms individual CNN by 2% in the overall classification accuracy. Also, it achieves an accuracy of 99.46% which is better than several state of the art results.