Multi-channel capsule network ensemble for plant disease detection
Abstract This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule...
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Online Access: | https://doi.org/10.1007/s42452-021-04694-2 |
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doaj-14e6c4aa347d4419bef613ea8bc71feb2021-06-20T11:20:03ZengSpringerSN Applied Sciences2523-39632523-39712021-06-013711010.1007/s42452-021-04694-2Multi-channel capsule network ensemble for plant disease detectionMusa Peker0Department of Artificial Intelligence/Machine LearningAbstract This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule networks, individually trained on images applied different preprocessing techniques and then combined together. In this way, the final ensemble can better detect plant diseases by making use of different attributes of the data. Our experiments carried out using a well-known data set and various state-of-the-art classification methods demonstrate that our classification approach can provide competitive advantages in terms of classification accuracy. Article Highlights An ensemble of capsule networks has been developed for the automated detection of plant diseases with high accuracies. Better accuracy has been achieved with ensemble learning compared to a single model With the proposed method, better results have been obtained compared to state-of-the-art classification methods in the literaturehttps://doi.org/10.1007/s42452-021-04694-2Multi-channel capsule network ensemblePlant disease detectionCapsule networksEnsemble learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Musa Peker |
spellingShingle |
Musa Peker Multi-channel capsule network ensemble for plant disease detection SN Applied Sciences Multi-channel capsule network ensemble Plant disease detection Capsule networks Ensemble learning |
author_facet |
Musa Peker |
author_sort |
Musa Peker |
title |
Multi-channel capsule network ensemble for plant disease detection |
title_short |
Multi-channel capsule network ensemble for plant disease detection |
title_full |
Multi-channel capsule network ensemble for plant disease detection |
title_fullStr |
Multi-channel capsule network ensemble for plant disease detection |
title_full_unstemmed |
Multi-channel capsule network ensemble for plant disease detection |
title_sort |
multi-channel capsule network ensemble for plant disease detection |
publisher |
Springer |
series |
SN Applied Sciences |
issn |
2523-3963 2523-3971 |
publishDate |
2021-06-01 |
description |
Abstract This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule networks, individually trained on images applied different preprocessing techniques and then combined together. In this way, the final ensemble can better detect plant diseases by making use of different attributes of the data. Our experiments carried out using a well-known data set and various state-of-the-art classification methods demonstrate that our classification approach can provide competitive advantages in terms of classification accuracy. Article Highlights An ensemble of capsule networks has been developed for the automated detection of plant diseases with high accuracies. Better accuracy has been achieved with ensemble learning compared to a single model With the proposed method, better results have been obtained compared to state-of-the-art classification methods in the literature |
topic |
Multi-channel capsule network ensemble Plant disease detection Capsule networks Ensemble learning |
url |
https://doi.org/10.1007/s42452-021-04694-2 |
work_keys_str_mv |
AT musapeker multichannelcapsulenetworkensembleforplantdiseasedetection |
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1721370067256475648 |