Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models
Taro, a vital crop in West Africa, is under attack by a disease called Taro Leaf Blight, which is bad news for the economy and farmer since it severely affects their income. Our study tackles the tough parts of spotting plant diseases, like the need for diverse datasets and better ways to analyze im...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524002417 |
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| author | Chidiebere Nwaneto Chika Yiinka-Banjo Ogban-Asuquo Ugot Thompson Annor Obiageli Umeugochukwu |
| author_facet | Chidiebere Nwaneto Chika Yiinka-Banjo Ogban-Asuquo Ugot Thompson Annor Obiageli Umeugochukwu |
| author_sort | Chidiebere Nwaneto |
| collection | DOAJ |
| description | Taro, a vital crop in West Africa, is under attack by a disease called Taro Leaf Blight, which is bad news for the economy and farmer since it severely affects their income. Our study tackles the tough parts of spotting plant diseases, like the need for diverse datasets and better ways to analyze images. We're focusing on making a top-notch dataset just for West Africa and using some of the latest tech in deep learning—like VGG16, ResNet50, InceptionV3, MobileNetV2, DenseNet121, Xception, and the Vision Transformer—to spot the disease. From our research, it turns out, the Vision Transformer is the star player here, nailing a 74% success rate in picking out the disease in images, which is way better than older methods like VGG16 and ResNet50 that scored 56% and 36%. In addition, we're digging into how this can help manage diseases in West African farms, facing current problems head-on and suggesting new ways to make things better, not just for Taro but other crops too. The findings are a win-win for tech and farming, offering solid plans to fight back against this blight and keep crops healthy. |
| format | Article |
| id | doaj-art-06dc52dfdf4540ed954afe618ec11664 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-06dc52dfdf4540ed954afe618ec116642025-08-20T01:59:35ZengElsevierSmart Agricultural Technology2772-37552024-12-01910063610.1016/j.atech.2024.100636Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification modelsChidiebere Nwaneto0Chika Yiinka-Banjo1Ogban-Asuquo Ugot2Thompson Annor3Obiageli Umeugochukwu4Computer Science Department, University of Lagos, Akoka, Lagos, NigeriaComputer Science Department, University of Lagos, Akoka, Lagos, NigeriaComputer Science Department, University of Lagos, Akoka, Lagos, NigeriaDepartment of Meteorology and Climate Science Kwame Nkrumah University of Science and Technology, Kumasi, GhanaUniversity of Nigeria, Nsukka, NigeriaTaro, a vital crop in West Africa, is under attack by a disease called Taro Leaf Blight, which is bad news for the economy and farmer since it severely affects their income. Our study tackles the tough parts of spotting plant diseases, like the need for diverse datasets and better ways to analyze images. We're focusing on making a top-notch dataset just for West Africa and using some of the latest tech in deep learning—like VGG16, ResNet50, InceptionV3, MobileNetV2, DenseNet121, Xception, and the Vision Transformer—to spot the disease. From our research, it turns out, the Vision Transformer is the star player here, nailing a 74% success rate in picking out the disease in images, which is way better than older methods like VGG16 and ResNet50 that scored 56% and 36%. In addition, we're digging into how this can help manage diseases in West African farms, facing current problems head-on and suggesting new ways to make things better, not just for Taro but other crops too. The findings are a win-win for tech and farming, offering solid plans to fight back against this blight and keep crops healthy.http://www.sciencedirect.com/science/article/pii/S2772375524002417Taro Leaf BlightDeep Learning ModelsImage Classification TechniquesVision TransformerWest African AgriculturePlant Disease Detection |
| spellingShingle | Chidiebere Nwaneto Chika Yiinka-Banjo Ogban-Asuquo Ugot Thompson Annor Obiageli Umeugochukwu Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models Smart Agricultural Technology Taro Leaf Blight Deep Learning Models Image Classification Techniques Vision Transformer West African Agriculture Plant Disease Detection |
| title | Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models |
| title_full | Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models |
| title_fullStr | Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models |
| title_full_unstemmed | Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models |
| title_short | Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models |
| title_sort | early detection of the taro leaf blight disease in the west african sub region using deep image classification models |
| topic | Taro Leaf Blight Deep Learning Models Image Classification Techniques Vision Transformer West African Agriculture Plant Disease Detection |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524002417 |
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