An implementation and analysis of deep learning models for the detection of wheat diseases
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2024
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| Linkit: | http://hdl.handle.net/10361/23611 |
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10361-236112024-06-26T21:01:36Z An implementation and analysis of deep learning models for the detection of wheat diseases Zubair, Ahmad Keya, Sharmin Akter Shailee, Tasnia Zarin Lenin, Syed Mahathir Md Nandi, Dhruba Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Deep learning Machine learning Wheat diseases Prediction Vision transformer Data mining Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-48). 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. Ahmad Zubair Sharmin Akter Keya Tasnia Zarin Shailee Syed Mahathir Md. Lenin Dhruba Nandi B.Sc in Computer Science 2024-06-26T10:25:14Z 2024-06-26T10:25:14Z ©2023 2023-09 Thesis ID 19101147 ID 19101147 ID 19101145 ID 18301268 ID 19301256 http://hdl.handle.net/10361/23611 en Brac 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. 58 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep learning Machine learning Wheat diseases Prediction Vision transformer Data mining Machine learning |
| spellingShingle |
Deep learning Machine learning Wheat diseases Prediction Vision transformer Data mining Machine learning Zubair, Ahmad Keya, Sharmin Akter Shailee, Tasnia Zarin Lenin, Syed Mahathir Md Nandi, Dhruba An implementation and analysis of deep learning models for the detection of wheat diseases |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Zubair, Ahmad Keya, Sharmin Akter Shailee, Tasnia Zarin Lenin, Syed Mahathir Md Nandi, Dhruba |
| format |
Thesis |
| author |
Zubair, Ahmad Keya, Sharmin Akter Shailee, Tasnia Zarin Lenin, Syed Mahathir Md Nandi, Dhruba |
| author_sort |
Zubair, Ahmad |
| title |
An implementation and analysis of deep learning models for the detection of wheat diseases |
| title_short |
An implementation and analysis of deep learning models for the detection of wheat diseases |
| title_full |
An implementation and analysis of deep learning models for the detection of wheat diseases |
| title_fullStr |
An implementation and analysis of deep learning models for the detection of wheat diseases |
| title_full_unstemmed |
An implementation and analysis of deep learning models for the detection of wheat diseases |
| title_sort |
implementation and analysis of deep learning models for the detection of wheat diseases |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23611 |
| work_keys_str_mv |
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1814307675997995008 |