Model for Effective Rice Disease Recognition Based on Deep Learning Techniques
Iraq’s primary crop, crucial for both domestic consumption and exports, is rice. The prevalence of rice infections poses a significant challenge to farmers, impacting crop yield and resulting in substantial losses. Human identification of diseases relies on expertise, making early diagnosis crucial...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00030.pdf |
| _version_ | 1797213289901457408 |
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| author | Bachay Firas Muneam AL_Dujaili Mohammed Jawad Al-Fatlawi Ahmed |
| author_facet | Bachay Firas Muneam AL_Dujaili Mohammed Jawad Al-Fatlawi Ahmed |
| author_sort | Bachay Firas Muneam |
| collection | DOAJ |
| description | Iraq’s primary crop, crucial for both domestic consumption and exports, is rice. The prevalence of rice infections poses a significant challenge to farmers, impacting crop yield and resulting in substantial losses. Human identification of diseases relies on expertise, making early diagnosis crucial for sustaining rice plant health. To address the limited number of rice leaf images in the database, our approach incorporates augmentation and dilation rate. Integrating drone technology and machine learning algorithms offers a promising solution to efficiently diagnose rice leaf diseases. However, existing methods face challenges such as picture backgrounds, insufficient field image data, and symptom variations. This work introduces a robust methodology, leveraging a specialized Convolutional Neural Network (CNN) model for rice leaf photos, effectively enhancing disease classification accuracy. The proposed approach successfully identifies and diagnoses three distinct classes: leaf smut, brown spot, and bacterial leaf blight. |
| first_indexed | 2024-04-24T10:55:55Z |
| format | Article |
| id | doaj.art-ff66f7d227254203b1888870d8a34f43 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:55:55Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-ff66f7d227254203b1888870d8a34f432024-04-12T07:36:28ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970003010.1051/bioconf/20249700030bioconf_iscku2024_00030Model for Effective Rice Disease Recognition Based on Deep Learning TechniquesBachay Firas Muneam0AL_Dujaili Mohammed Jawad1Al-Fatlawi Ahmed2Department of Scientific Affair, University of KufaDepartment of Electronic and Communication, Faculty of Engineering, University of KufaComputer Technical Engineering Dept., Technical Engineering College, University of AlkafeelIraq’s primary crop, crucial for both domestic consumption and exports, is rice. The prevalence of rice infections poses a significant challenge to farmers, impacting crop yield and resulting in substantial losses. Human identification of diseases relies on expertise, making early diagnosis crucial for sustaining rice plant health. To address the limited number of rice leaf images in the database, our approach incorporates augmentation and dilation rate. Integrating drone technology and machine learning algorithms offers a promising solution to efficiently diagnose rice leaf diseases. However, existing methods face challenges such as picture backgrounds, insufficient field image data, and symptom variations. This work introduces a robust methodology, leveraging a specialized Convolutional Neural Network (CNN) model for rice leaf photos, effectively enhancing disease classification accuracy. The proposed approach successfully identifies and diagnoses three distinct classes: leaf smut, brown spot, and bacterial leaf blight.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00030.pdf |
| spellingShingle | Bachay Firas Muneam AL_Dujaili Mohammed Jawad Al-Fatlawi Ahmed Model for Effective Rice Disease Recognition Based on Deep Learning Techniques BIO Web of Conferences |
| title | Model for Effective Rice Disease Recognition Based on Deep Learning Techniques |
| title_full | Model for Effective Rice Disease Recognition Based on Deep Learning Techniques |
| title_fullStr | Model for Effective Rice Disease Recognition Based on Deep Learning Techniques |
| title_full_unstemmed | Model for Effective Rice Disease Recognition Based on Deep Learning Techniques |
| title_short | Model for Effective Rice Disease Recognition Based on Deep Learning Techniques |
| title_sort | model for effective rice disease recognition based on deep learning techniques |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00030.pdf |
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