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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Main Authors: Chidiebere Nwaneto, Chika Yiinka-Banjo, Ogban-Asuquo Ugot, Thompson Annor, Obiageli Umeugochukwu
Format: Article
Language:English
Published: Elsevier 2024-12-01
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.
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publisher Elsevier
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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
work_keys_str_mv AT chidieberenwaneto earlydetectionofthetaroleafblightdiseaseinthewestafricansubregionusingdeepimageclassificationmodels
AT chikayiinkabanjo earlydetectionofthetaroleafblightdiseaseinthewestafricansubregionusingdeepimageclassificationmodels
AT ogbanasuquougot earlydetectionofthetaroleafblightdiseaseinthewestafricansubregionusingdeepimageclassificationmodels
AT thompsonannor earlydetectionofthetaroleafblightdiseaseinthewestafricansubregionusingdeepimageclassificationmodels
AT obiageliumeugochukwu earlydetectionofthetaroleafblightdiseaseinthewestafricansubregionusingdeepimageclassificationmodels