Plant disease detection using convolutional neural network
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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| Jazyk: | English |
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Brac University
2021
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| On-line přístup: | http://hdl.handle.net/10361/14732 |
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10361-147322022-01-26T10:18:15Z Plant disease detection using convolutional neural network Hossain, Mohammad Shifat Noor, Fatin Ishraq Ali, Mir Ayman Alam, Ra ul Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Rice plant disease Detection Prediction ResNet- 152 Convolutional Neural Network Machine learning 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 24-26). Rice is a staple crop of Bangladesh and many metric tons of it are being destroyed every year due to diseases. If the diseases can be efficiently and accurately classified ed and recognized at early stage, the farmers can get the required help resulting in better rice crop yields. Thus, in an attempt to better increase the rice crop, yield our proposal is to make a website prototype system by using different machine learning algorithms to analyze and recognize different rice crop diseases. By utilizing CNN and its variations for the detection of rice plant diseases, we aim to guide individuals and assist farmers in identifying the infected plants early. By doing so, automated systems can be made to find out the infected crops and suggest diagnosis based on the problems. The photographs of rice plant leaves are taken for brown spot, Hispa and leaf blast diseases. We have used Convolution Neural Network (CNN) which comprises of different layers which are used for prediction. In addition, we have implemented other 4 CNN structures such as GoogleNet, RestNet-152 and VGG19 which is 19-layer deep structure. On the other hand, the features from the infected area are extracted using Histogram Oriented Gradient (HOG) features and for distinguishing between their category these features were given to the Support Vector Machine (SVM). To sum up, by experimentation we will be able to conclude which structure or algorithm has the most success rate. As a result, by this approach the information will be provide at the initial stage so that one can take necessary steps at the beginning to prevent the rice plant diseases and minimize the loss of production. Mohammad Shifat Hossain Fatin Ishraq Noor Mir Ayman Ali Ra ul Alam B. Computer Science 2021-07-03T19:21:18Z 2021-07-03T19:21:18Z 2020 2020-04 Thesis ID 15101044 ID 15301086 15101104 ID 15101130 http://hdl.handle.net/10361/14732 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. 26 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Rice plant disease Detection Prediction ResNet- 152 Convolutional Neural Network Machine learning |
| spellingShingle |
Rice plant disease Detection Prediction ResNet- 152 Convolutional Neural Network Machine learning Hossain, Mohammad Shifat Noor, Fatin Ishraq Ali, Mir Ayman Alam, Ra ul Plant disease detection using convolutional neural network |
| 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 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Hossain, Mohammad Shifat Noor, Fatin Ishraq Ali, Mir Ayman Alam, Ra ul |
| format |
Thesis |
| author |
Hossain, Mohammad Shifat Noor, Fatin Ishraq Ali, Mir Ayman Alam, Ra ul |
| author_sort |
Hossain, Mohammad Shifat |
| title |
Plant disease detection using convolutional neural network |
| title_short |
Plant disease detection using convolutional neural network |
| title_full |
Plant disease detection using convolutional neural network |
| title_fullStr |
Plant disease detection using convolutional neural network |
| title_full_unstemmed |
Plant disease detection using convolutional neural network |
| title_sort |
plant disease detection using convolutional neural network |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14732 |
| work_keys_str_mv |
AT hossainmohammadshifat plantdiseasedetectionusingconvolutionalneuralnetwork AT noorfatinishraq plantdiseasedetectionusingconvolutionalneuralnetwork AT alimirayman plantdiseasedetectionusingconvolutionalneuralnetwork AT alamraul plantdiseasedetectionusingconvolutionalneuralnetwork |
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