Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease
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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| Idioma: | English |
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Brac University
2024
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| Acesso em linha: | http://hdl.handle.net/10361/23624 |
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10361-236242024-07-01T21:03:44Z Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease Khan, Shama Arafat, Mohammad Dein, Mosleh Al Niloy, Md. Tifur Waesh Mahmud, Mufrad Alam, Md. Ashraful Rahman, Rafeed Department of Computer Science and Engineering, Brac University Maize Corn Crop Leaf disease Accuracy Deep convolutional neural network Neural networks (Computer science) Corn Crops and climate 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 39-41). Maize is one of the most produced crops in the world and a significant contributor to the economy of various countries. Maize leaf diseases can lead to hamper of crop production and eventually reduce profit of agricultural farms. Through accurately identifying maize leaf disease earlier, farmers can take the necessary steps to minimize damages. In this paper, we propose to incorporate features extracted from deep convolutional neural networks and train them using machine learning classifiers for the identification of maize leaf diseases with high accuracy. For feature extraction, we trained 5 CNN models, which are InceptionResNetV2, DenseNet121, EfficientNetV2S, Xception and InceptionV3, reaching accuracy of 99.172%, 98.965%, 98.654%, 98.344% and 98.965%. Furthermore, the features extracted using these models were used to train K-Nearest Neighbors and Support Vector Classifier. The K-Nearest Neighbors classifier reach an accuracy of 99.586%, while the Support Vector Classifier reached an accuracy of 99.379%. Shama Khan Mohammad Arafat Mosleh Al Dein Md. Tifur Waesh Niloy Mufrad Mahmud B.Sc in Computer Science 2024-07-01T09:12:35Z 2024-07-01T09:12:35Z ©2023 2023-09 Thesis ID 19201142 ID 20101281 ID 20101313 ID 20101314 ID 20101316 http://hdl.handle.net/10361/23624 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. 51 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Maize Corn Crop Leaf disease Accuracy Deep convolutional neural network Neural networks (Computer science) Corn Crops and climate |
| spellingShingle |
Maize Corn Crop Leaf disease Accuracy Deep convolutional neural network Neural networks (Computer science) Corn Crops and climate Khan, Shama Arafat, Mohammad Dein, Mosleh Al Niloy, Md. Tifur Waesh Mahmud, Mufrad Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease |
| 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 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Khan, Shama Arafat, Mohammad Dein, Mosleh Al Niloy, Md. Tifur Waesh Mahmud, Mufrad |
| format |
Thesis |
| author |
Khan, Shama Arafat, Mohammad Dein, Mosleh Al Niloy, Md. Tifur Waesh Mahmud, Mufrad |
| author_sort |
Khan, Shama |
| title |
Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease |
| title_short |
Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease |
| title_full |
Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease |
| title_fullStr |
Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease |
| title_full_unstemmed |
Incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of Maize Leaf Disease |
| title_sort |
incorporating deep features extracted from convolutional neural networks to utilize machine learning classifiers for improved identification of maize leaf disease |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23624 |
| work_keys_str_mv |
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