A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices

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dc.contributor.authorMinh, Hoang Trong
dc.contributor.authorAnh, Tuan Pham
dc.contributor.authorNhan, Van Nguyen
dc.coverage.issue2cs
dc.coverage.volume28cs
dc.date.accessioned2023-01-13T06:21:06Z
dc.date.available2023-01-13T06:21:06Z
dc.date.issued2022-12-20cs
dc.description.abstractOne of the essential aspects of smart farming and precision agriculture is quickly and accurately identifying diseases. Utilizing plant imaging and recently developed machine learning algorithms, the timely detection of diseases provides many benefits to farmers regarding crop and product quality. Specifically, for farmers in remote areas, disease diagnostics on edge devices is the most effective and optimal method to handle crop damage as quickly as possible. However, the limitations posed by the equipment’s limited resources have reduced the accuracy of disease detection. Consequently, adopting an efficient machine-learning model and decreasing the model size to fit the edge device is an exciting problem that receives significant attention from researchers and developers. This work takes advantage of previous research on deep learning model performance evaluation to present a model that applies to both the Plant-Village laboratory dataset and the Plant-Doc natural-type dataset. The evaluation results indicate that the proposed model is as effective as the current state-of-the-art model. Moreover, due to the quantization technique, the system performance stays the same when the model size is reduced to accommodate the edge device.en
dc.formattextcs
dc.format.extent41-48cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2022 vol. 28, č. 2, s. 41-48. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2022.2.041en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/208746
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/199cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectDeep Convolution Neuron Networksen
dc.subjectEdge Computingen
dc.subjectMulti-leaf disease imageen
dc.subjectPlant-Doc dataseten
dc.titleA Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devicesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 28, No. 2