Cassava leaf disease classification using deep learning and convolutional neural network ensemble
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| פורמט: | Thesis |
| שפה: | English |
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Brac University
2022
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| גישה מקוונת: | http://hdl.handle.net/10361/16633 |
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10361-16633 |
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10361-166332022-05-18T21:01:33Z Cassava leaf disease classification using deep learning and convolutional neural network ensemble Shahriar, Hasan Shuvo, Protick Sarker Fahim, Md. Saidul Haque Sordar, Md Sobuj Haque, Md Esadul Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Deep learning Cassava leaf Prediction Decision tree Xception Neural networks EfficientNet B0 Resnet 50 VGG16 Inception V3 DenseNet 121 Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-35). Cassava is a high-protein and nutrient-dense plant, notably inside the leaves. Cassava is often used as a rice alternative. Pests, viruses, bacteria, and fungus may cause a variety of illnesses on cassava leaves. This study consists of four main diseases that commonly affect cassava leaves: Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD) and we took these four diseases as labels in our research. Furthermore, we took 22000 infected images from Kaggle and we have transformed our dataset into four different image transformation to ensure the accuracy of our model. These four different augmentations are Random Crop Augmentation, Random Flip Augmentation, Random Rotation Augmentation and Random Contrast Augmentation. Finally, we used six algorithms to detect the diseases of cassava leaves. These six algorithms are Xception, EfficientNetB0 Resnet50, VGG16 Densenet121, InceptionV3. While we operated these algorithms on our trained dataset, it gave diverse precision. For the Xception, it gave 91.3% accuracy, EfficientNetB0:91.1%, ResNet50: 85.0 %, VGG16: 68.0 %, DenseNet121: 87.0 % and for the InceptionV3, it gave 86.4 % precision respectively. Here, not every one of the algorithms performed well. Xception and EfficientNetB0 have the most noteworthy accuracy among these. Hasan Shahriar Protick Sarker Shuvo Md. Saidul Haque Fahim Md Sobuj Sordar Md Esadul Haque B. Computer Science 2022-05-18T04:18:38Z 2022-05-18T04:18:38Z 2022 2022-01 Thesis ID 20301476 ID 16301078 ID 16301053 ID 17301148 ID 16201032 http://hdl.handle.net/10361/16633 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. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep learning Cassava leaf Prediction Decision tree Xception Neural networks EfficientNet B0 Resnet 50 VGG16 Inception V3 DenseNet 121 Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science) |
| spellingShingle |
Deep learning Cassava leaf Prediction Decision tree Xception Neural networks EfficientNet B0 Resnet 50 VGG16 Inception V3 DenseNet 121 Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science) Shahriar, Hasan Shuvo, Protick Sarker Fahim, Md. Saidul Haque Sordar, Md Sobuj Haque, Md Esadul Cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Shahriar, Hasan Shuvo, Protick Sarker Fahim, Md. Saidul Haque Sordar, Md Sobuj Haque, Md Esadul |
| format |
Thesis |
| author |
Shahriar, Hasan Shuvo, Protick Sarker Fahim, Md. Saidul Haque Sordar, Md Sobuj Haque, Md Esadul |
| author_sort |
Shahriar, Hasan |
| title |
Cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| title_short |
Cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| title_full |
Cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| title_fullStr |
Cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| title_full_unstemmed |
Cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| title_sort |
cassava leaf disease classification using deep learning and convolutional neural network ensemble |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/16633 |
| work_keys_str_mv |
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