Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model
Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production....
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/11/17/2230 |
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| author | Ghazanfar Latif Sherif E. Abdelhamid Roxane Elias Mallouhy Jaafar Alghazo Zafar Abbas Kazimi |
| author_facet | Ghazanfar Latif Sherif E. Abdelhamid Roxane Elias Mallouhy Jaafar Alghazo Zafar Abbas Kazimi |
| author_sort | Ghazanfar Latif |
| collection | DOAJ |
| description | Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature. |
| first_indexed | 2024-03-10T01:24:06Z |
| format | Article |
| id | doaj.art-a0182ad7d57747e08a493d091a1554fa |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-10T01:24:06Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-a0182ad7d57747e08a493d091a1554fa2023-11-23T13:55:10ZengMDPI AGPlants2223-77472022-08-011117223010.3390/plants11172230Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN ModelGhazanfar Latif0Sherif E. Abdelhamid1Roxane Elias Mallouhy2Jaafar Alghazo3Zafar Abbas Kazimi4Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi ArabiaDepartment of Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USADepartment of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi ArabiaDepartment of Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USADepartment of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi ArabiaRice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.https://www.mdpi.com/2223-7747/11/17/2230deep learningtransfer learningplant leaf disease detectionrice leaf disease detectionconvolutional neural networksVGG19 |
| spellingShingle | Ghazanfar Latif Sherif E. Abdelhamid Roxane Elias Mallouhy Jaafar Alghazo Zafar Abbas Kazimi Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model Plants deep learning transfer learning plant leaf disease detection rice leaf disease detection convolutional neural networks VGG19 |
| title | Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model |
| title_full | Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model |
| title_fullStr | Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model |
| title_full_unstemmed | Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model |
| title_short | Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model |
| title_sort | deep learning utilization in agriculture detection of rice plant diseases using an improved cnn model |
| topic | deep learning transfer learning plant leaf disease detection rice leaf disease detection convolutional neural networks VGG19 |
| url | https://www.mdpi.com/2223-7747/11/17/2230 |
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