Hossain, Muhammad IqbalZubair, AhmadKeya, Sharmin AkterShailee, Tasnia ZarinLenin, Syed Mahathir MdNandi, Dhruba2024-06-262024-06-26©20232023-09ID 19101147ID 19101147ID 19101145ID 18301268ID 19301256http://hdl.handle.net/10361/23611Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-48).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.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 ViT and a hybrid 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 gets the highest accuracy of 99.34% among all the models.58 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Deep learningMachine learningWheat diseasesPredictionVision transformerData miningMachine learningThesis