Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests

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dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidSengur, Abdulkadir/Q-8023-2019
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorHanbay, Davut
dc.contributor.authorSengur, Abdulkadir
dc.date.accessioned2024-08-04T20:46:57Z
dc.date.available2024-08-04T20:46:57Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination of LSTM network with pre-trained CNN models. Specifically, in transfer learning, we adopted deep feature extraction from various fully connected layers of these pre-trained deep models. AlexNet, GoogleNet and DenseNet201 models are used in this work for feature extraction. The extracted deep features are then fed into the LSTM layer in order to construct a robust hybrid model for apple disease and pest detection. Later, the output predictions of three LSTM layers determined the class labels of the input images by majority voting classifier. In addition, we use an automatic scheme for determining the best choice of the network parameters of the LSTM layer. The experiments are carried out using data consisting of real-time apple disease and pest images from Turkey and the accuracy rates are calculated for performance evaluation. The experimental results show that by using the proposed ensemble combination structure, the results are comparable to, or better than, the pre-trained deep architectures.en_US
dc.identifier.doi10.1007/s12652-019-01591-w
dc.identifier.issn1868-5137
dc.identifier.issn1868-5145
dc.identifier.scopus2-s2.0-85075444033en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s12652-019-01591-w
dc.identifier.urihttps://hdl.handle.net/11616/99069
dc.identifier.wosWOS:000574444900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal of Ambient Intelligence and Humanized Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPlant diseases and pests detectionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learning architecturesen_US
dc.subjectDeep featuresen_US
dc.subjectLSTMen_US
dc.titleMulti-model LSTM-based convolutional neural networks for detection of apple diseases and pestsen_US
dc.typeArticleen_US

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