An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset
Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and dis...
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MDPI AG
2023-09-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/11/5/116 |
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| author | Eric Hitimana Omar Janvier Sinayobye J. Chrisostome Ufitinema Jane Mukamugema Peter Rwibasira Theoneste Murangira Emmanuel Masabo Lucy Cherono Chepkwony Marie Cynthia Abijuru Kamikazi Jeanne Aline Ukundiwabo Uwera Simon Martin Mvuyekure Gaurav Bajpai Jackson Ngabonziza |
| author_facet | Eric Hitimana Omar Janvier Sinayobye J. Chrisostome Ufitinema Jane Mukamugema Peter Rwibasira Theoneste Murangira Emmanuel Masabo Lucy Cherono Chepkwony Marie Cynthia Abijuru Kamikazi Jeanne Aline Ukundiwabo Uwera Simon Martin Mvuyekure Gaurav Bajpai Jackson Ngabonziza |
| author_sort | Eric Hitimana |
| collection | DOAJ |
| description | Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics. |
| first_indexed | 2024-03-10T20:51:19Z |
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| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T20:51:19Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-df5191c189514e29b2b937a43751b7b82023-11-19T18:20:05ZengMDPI AGTechnologies2227-70802023-09-0111511610.3390/technologies11050116An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica DatasetEric Hitimana0Omar Janvier Sinayobye1J. Chrisostome Ufitinema2Jane Mukamugema3Peter Rwibasira4Theoneste Murangira5Emmanuel Masabo6Lucy Cherono Chepkwony7Marie Cynthia Abijuru Kamikazi8Jeanne Aline Ukundiwabo Uwera9Simon Martin Mvuyekure10Gaurav Bajpai11Jackson Ngabonziza12Department of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, RwandaDepartment of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, RwandaDepartment of Biology, University of Rwanda, Kigali P.O. Box 3900, RwandaDepartment of Biology, University of Rwanda, Kigali P.O. Box 3900, RwandaDepartment of Biology, University of Rwanda, Kigali P.O. Box 3900, RwandaDepartment of Computer Science, University of Rwanda, Kigali P.O. Box 2285, RwandaDepartment of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, RwandaAfrican Center of Excellence in Data Science, University of Rwanda, Kigali P.O. Box 4285, RwandaDepartment of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, RwandaDepartment of Computer and Software Engineering, University of Rwanda, Kigali P.O. Box 3900, RwandaRwanda Agriculture Board, Kicukiro District, Rubilizi, Kigali P.O. Box 5016, RwandaDirectorate of Grants and Partnership, Kampala International University, Ggaba Road, Kansanga, Kampala P.O. Box 20000, UgandaBank of Kigali Plc, Kigali P.O. Box 175, RwandaRwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics.https://www.mdpi.com/2227-7080/11/5/116coffee leaf diseasesarabica coffeedeep learningVGG16DenseNet |
| spellingShingle | Eric Hitimana Omar Janvier Sinayobye J. Chrisostome Ufitinema Jane Mukamugema Peter Rwibasira Theoneste Murangira Emmanuel Masabo Lucy Cherono Chepkwony Marie Cynthia Abijuru Kamikazi Jeanne Aline Ukundiwabo Uwera Simon Martin Mvuyekure Gaurav Bajpai Jackson Ngabonziza An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset Technologies coffee leaf diseases arabica coffee deep learning VGG16 DenseNet |
| title | An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset |
| title_full | An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset |
| title_fullStr | An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset |
| title_full_unstemmed | An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset |
| title_short | An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset |
| title_sort | intelligent system based coffee plant leaf disease recognition using deep learning techniques on rwandan arabica dataset |
| topic | coffee leaf diseases arabica coffee deep learning VGG16 DenseNet |
| url | https://www.mdpi.com/2227-7080/11/5/116 |
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