Tomato leaf disease detection using Resnet-50 and MobileNet Architecture
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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| التنسيق: | أطروحة |
| اللغة: | en_US |
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Brac University
2021
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| الوصول للمادة أونلاين: | http://dspace.bracu.ac.bd/xmlui/handle/10361/14448 |
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10361-14448 |
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10361-144482022-01-26T10:10:24Z Tomato leaf disease detection using Resnet-50 and MobileNet Architecture Tahamid, Abu Arif, Hossain Islam, Md. Saiful Department of Computer Science and Engineering, Brac University Transfer learning MobileNet architecture Resnet-50 architecture Deep learning Fine tuning Segmentation This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-31). Diseases in Tomato mostly on the leaves affect the reduction of both the standard and quantity of agricultural products. Several diseases such as bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, two-spotted spider mite, yellow leaf curl Virus, mosaic virus common diseases found in tomato, thus, real-time and precise recognition technology is essential. To detect plant leaf diseases, image processing techniques such as image acquisition, segmentation through two technical models Resent50 and MobileNet are implemented. These two methods are implemented by the transfer learning method which widely used for deep learning, where every step is get improved than the previous one. The deeper stages the execution goes, the more accurate result tends to yield. In Resnet-50 Model, experimental results fluctuate from 94 percent to 99.81% and In MobileNet the predictions correction resonates within 95.23% to a maximum of 99.88% which buttress the prediction with respect to the actual data by analyzing accuracy and execution time to identify leaf diseases. Abu Tahamid B. Computer Science 2021-05-29T16:56:35Z 2021-05-29T16:56:35Z 2020 2020-04 Thesis ID: 19341015 http://dspace.bracu.ac.bd/xmlui/handle/10361/14448 en_US 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. 31 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
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en_US |
| topic |
Transfer learning MobileNet architecture Resnet-50 architecture Deep learning Fine tuning Segmentation |
| spellingShingle |
Transfer learning MobileNet architecture Resnet-50 architecture Deep learning Fine tuning Segmentation Tahamid, Abu Tomato leaf disease detection using Resnet-50 and MobileNet Architecture |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Tahamid, Abu |
| format |
Thesis |
| author |
Tahamid, Abu |
| author_sort |
Tahamid, Abu |
| title |
Tomato leaf disease detection using Resnet-50 and MobileNet Architecture |
| title_short |
Tomato leaf disease detection using Resnet-50 and MobileNet Architecture |
| title_full |
Tomato leaf disease detection using Resnet-50 and MobileNet Architecture |
| title_fullStr |
Tomato leaf disease detection using Resnet-50 and MobileNet Architecture |
| title_full_unstemmed |
Tomato leaf disease detection using Resnet-50 and MobileNet Architecture |
| title_sort |
tomato leaf disease detection using resnet-50 and mobilenet architecture |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://dspace.bracu.ac.bd/xmlui/handle/10361/14448 |
| work_keys_str_mv |
AT tahamidabu tomatoleafdiseasedetectionusingresnet50andmobilenetarchitecture |
| _version_ |
1814307640528863232 |