A Deep Features Extraction Model Based on the Transfer Learning Model and Vision Transformer “TLMViT” for Plant Disease Classification

This paper proposes a novel approach for extracting deep features and classifying diseased plant leaves. The agriculture industry is negatively impacted by plant diseases causing crop and economic loss. Accurate and timely diagnosis is crucial for managing and controlling plant diseases, as traditional methods can be costly and time-consuming. Deep learning-based tools effectively detect plant diseases depending on the qualitative of extracted features. In this regard, a hybrid model for plant disease classification based on a Transfer Learning-based model followed by a vision transformer (TLMViT) is proposed. TLMViT has four stages: 1) data acquisition, where the PlantVillage and wheat datasets are used to train and evaluate the proposed model, 2) image augmentation to increase the number of training samples and overcome the overfitting issue, 3) leaf feature extraction by two consecutive phases: initial features extraction by using pre-trained based model and deep features extraction by using ViT model, and 4) classification by using MLP classifier. TLMViT is experimented with using five pre-trained-based models followed by ViT individually. TLMViT performs accurately in plant disease classification, obtaining 98.81% and 99.86% validation accuracy for VGG19 followed by the ViT model on PlantVillage and wheat datasets respectively. Moreover, TLMViT is compared with pre-trained-based architecture. The comparison result illustrates that TLMViT achieved an enhancement of 1.11% and 1.099% in validation accuracy, 2.576% and 2.92% in validation loss compared with the transfer learning-based model for PlantVillage and wheat datasets respectively. Thereby proposed model proves the efficiency of using ViT for extracting deep features from the leaf.