Plant leaf disease identification
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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| 在线阅读: | http://hdl.handle.net/10361/22178 |
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10361-221782024-01-17T21:02:39Z Plant leaf disease identification Hasan Mahin, Mohammad Rakibul Moonwar, Waheed Rayhan Chy, Md. Shamsul Shahriar, Md. Fahim Rafi, Fahim Faisal Rasel, Annajiat Alim Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Neural network Convolutional Neural Network (CNN) Plant leaf disease identification Deep learning XAI Image processing Cognitive learning theory (Deep learning) Plant diseases--Diagnosis 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 76-83). Agriculture has consistently been an essential component of our day-to-day life over the centuries. Because of its contribution to our country’s revenue, the importance of agriculture has been steadily growing over the course of the years. However, there are some counter factors that prevent us from reaping the full benefits that crops have to offer. The presence of a wide variety of natural diseases on plant leaves is one such factor. The most prominent causes of these problems are typically severe weather conditions and excessive use of pesticides, both of which put a strain on the economy of Bangladesh as a whole. To reduce the severity of the problem, we are going to design an image processing system that utilizes Deep Learning and Convolutional Neural Networks (CNN) to classify plant leaf diseases. Our primary demographic of interest consists of farmers and other people willing to tend to crops. We have concluded that the best way to go about this is by constructing a website and making it as simple and straightforward as possible. The user will select im ages of the diseased leaf, and our CNN model will predict and categorize the leaf’s condition based on the chosen images. After implementing CNN, we introduce another model, namely LIME, which is based on the concept of Explainable AI (XAI). An XAI is an artificial intelligence that mainly helps humans to understand the decisions or predictions made by an AI. In this scenario, after our CNN model classifies the diseased leaves, the XAI aids us in understanding the reason and cause behind the leaves mentioned above being classified as how they are by the CNN model. Conclusively, following the completion of running our models, we managed to get a 99.54% accuracy rate in our testing phase. Mohammad Rakibul Hasan Mahin Waheed Moonwar Md. Shamsul Rayhan Chy Md. Fahim Shahriar Fahim Faisal Rafi B.Sc. in Computer Science and Engineering 2024-01-17T08:14:18Z 2024-01-17T08:14:18Z 2023 2023-01 Thesis ID: 20201220 ID: 20201219 ID: 19201109 ID: 19201046 ID: 19201081 http://hdl.handle.net/10361/22178 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. 83 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Neural network Convolutional Neural Network (CNN) Plant leaf disease identification Deep learning XAI Image processing Cognitive learning theory (Deep learning) Plant diseases--Diagnosis |
| spellingShingle |
Neural network Convolutional Neural Network (CNN) Plant leaf disease identification Deep learning XAI Image processing Cognitive learning theory (Deep learning) Plant diseases--Diagnosis Hasan Mahin, Mohammad Rakibul Moonwar, Waheed Rayhan Chy, Md. Shamsul Shahriar, Md. Fahim Rafi, Fahim Faisal Plant leaf disease identification |
| 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 |
Rasel, Annajiat Alim |
| author_facet |
Rasel, Annajiat Alim Hasan Mahin, Mohammad Rakibul Moonwar, Waheed Rayhan Chy, Md. Shamsul Shahriar, Md. Fahim Rafi, Fahim Faisal |
| format |
Thesis |
| author |
Hasan Mahin, Mohammad Rakibul Moonwar, Waheed Rayhan Chy, Md. Shamsul Shahriar, Md. Fahim Rafi, Fahim Faisal |
| author_sort |
Hasan Mahin, Mohammad Rakibul |
| title |
Plant leaf disease identification |
| title_short |
Plant leaf disease identification |
| title_full |
Plant leaf disease identification |
| title_fullStr |
Plant leaf disease identification |
| title_full_unstemmed |
Plant leaf disease identification |
| title_sort |
plant leaf disease identification |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22178 |
| work_keys_str_mv |
AT hasanmahinmohammadrakibul plantleafdiseaseidentification AT moonwarwaheed plantleafdiseaseidentification AT rayhanchymdshamsul plantleafdiseaseidentification AT shahriarmdfahim plantleafdiseaseidentification AT rafifahimfaisal plantleafdiseaseidentification |
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