Neural network based ensemble learning model for elevating wheat disease classification

Abstract

Detecting a disease visually is a time-consuming and error-prone operation, and in the agricultural arena, for disease control, crop yield loss prediction, and global food security, automatic and accurate evaluation of disease severity in crops is a particularly demanding study area. Deep Learning (DL), the latest innovation in the era of Artificial Intelligence (AI), is promising for fine-grained categorization of crop diseases since it eliminates labor-intensive feature extraction and segmentation. To diagnose the disease from photos, multiple pretrained models which are ResNet50, EfficientNetB0 and InceptionV3 along with Vision Transformer (ViT) and a hybrid Convolutional Neural Network (CNN) model have been trained on the wheat disease dataset. Again, an ensemble model of the hybrid CNN and the ViT has been proposed which has been compared with all the other models and the proposed model elevates the accuracy to 99.34%, which is the highest among all the accuracies of other models.