Academic Journal
Detection: A convolutional network method for plant disease recognition
| Τίτλος: | Detection: A convolutional network method for plant disease recognition |
|---|---|
| Συγγραφείς: | Albert, Enow Takang Achuo, Bille, Ngalle, Leonard, Ngonkeu Mangaptche Eddy |
| Στοιχεία εκδότη: | Pensoft Publishers |
| Έτος έκδοσης: | 2023 |
| Συλλογή: | Zenodo |
| Θεματικοί όροι: | plant disease recognition, convnets, feature extraction layers, ablation testing |
| Περιγραφή: | Within the last decade and a half, ConvNet models have shown increasingly impressive performances on image classification tasks. The continuous quest for better performing ConvNet models is of great importance in that it provides an avenue for the development of superior models which would serve to improve many much-needed services to humankind in domains such as crop pest and disease detection. This paper proposes a feature extraction ConvNet, called Detection, with aim to improve plant disease recognition. Detection contains four convolutional layers, four batch normalization layers and two max-pooling layers. For reference, its plant disease recognition performance was compared with the feature extraction layers for AlexNet, LeNet-5, ZFNet and VGGNet on four datasets from PlantVillage: bacterial spot disease (bell pepper), late blight disease (tomato), leaf mold disease (tomato) and yellow leaf curl disease (tomato). Prior to ablation testing, it achieved the second-highest overall classification accuracy over all four disease recognition datasets (65.50%) after AlexNet (84.33%). However, ablation tests revealed that the removal of the second convolutional layer from the network resulted in a 24.08% increase in overall accuracy on all four disease recognition datasets, up from 65.50% accuracy without the ablation. This surpassed AlexNet's overall accuracy on all four disease recognition datasets (84.33%) by 5.25%. Also, the removal of the second pooling layer from the network resulted in a 23.33% increase in overall accuracy on all four disease recognition datasets, up from 65.50% accuracy without the ablation. This also surpassed AlexNet's overall accuracy on all four disease recognition datasets (84.33%) by 4.40%. The results suggest that the proposed feature extraction ConvNet is a performant method for plant disease recognition. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | unknown |
| Relation: | https://zenodo.org/communities/biosyslit/; https://zenodo.org/records/16405095; oai:zenodo.org:16405095 |
| DOI: | 10.25081/ia.2023-04 |
| Διαθεσιμότητα: | https://doi.org/10.25081/ia.2023-04 https://zenodo.org/records/16405095 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Αριθμός Καταχώρησης: | edsbas.3BEB478C |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.25081/ia.2023-04# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.3BEB478C RelevancyScore: 926 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 926.330688476563 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Detection: A convolutional network method for plant disease recognition – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Albert%2C+Enow+Takang+Achuo%22">Albert, Enow Takang Achuo</searchLink><br /><searchLink fieldCode="AR" term="%22Bille%2C+Ngalle%22">Bille, Ngalle</searchLink><br /><searchLink fieldCode="AR" term="%22Leonard%2C+Ngonkeu+Mangaptche+Eddy%22">Leonard, Ngonkeu Mangaptche Eddy</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: Pensoft Publishers – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Zenodo – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22plant+disease+recognition%22">plant disease recognition</searchLink><br /><searchLink fieldCode="DE" term="%22convnets%22">convnets</searchLink><br /><searchLink fieldCode="DE" term="%22feature+extraction+layers%22">feature extraction layers</searchLink><br /><searchLink fieldCode="DE" term="%22ablation+testing%22">ablation testing</searchLink> – Name: Abstract Label: Description Group: Ab Data: Within the last decade and a half, ConvNet models have shown increasingly impressive performances on image classification tasks. The continuous quest for better performing ConvNet models is of great importance in that it provides an avenue for the development of superior models which would serve to improve many much-needed services to humankind in domains such as crop pest and disease detection. This paper proposes a feature extraction ConvNet, called Detection, with aim to improve plant disease recognition. Detection contains four convolutional layers, four batch normalization layers and two max-pooling layers. For reference, its plant disease recognition performance was compared with the feature extraction layers for AlexNet, LeNet-5, ZFNet and VGGNet on four datasets from PlantVillage: bacterial spot disease (bell pepper), late blight disease (tomato), leaf mold disease (tomato) and yellow leaf curl disease (tomato). Prior to ablation testing, it achieved the second-highest overall classification accuracy over all four disease recognition datasets (65.50%) after AlexNet (84.33%). However, ablation tests revealed that the removal of the second convolutional layer from the network resulted in a 24.08% increase in overall accuracy on all four disease recognition datasets, up from 65.50% accuracy without the ablation. This surpassed AlexNet's overall accuracy on all four disease recognition datasets (84.33%) by 5.25%. Also, the removal of the second pooling layer from the network resulted in a 23.33% increase in overall accuracy on all four disease recognition datasets, up from 65.50% accuracy without the ablation. This also surpassed AlexNet's overall accuracy on all four disease recognition datasets (84.33%) by 4.40%. The results suggest that the proposed feature extraction ConvNet is a performant method for plant disease recognition. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: unknown – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://zenodo.org/communities/biosyslit/; https://zenodo.org/records/16405095; oai:zenodo.org:16405095 – Name: DOI Label: DOI Group: ID Data: 10.25081/ia.2023-04 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.25081/ia.2023-04<br />https://zenodo.org/records/16405095 – Name: Copyright Label: Rights Group: Cpyrght Data: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode – Name: AN Label: Accession Number Group: ID Data: edsbas.3BEB478C |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3BEB478C |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.25081/ia.2023-04 Languages: – Text: unknown Subjects: – SubjectFull: plant disease recognition Type: general – SubjectFull: convnets Type: general – SubjectFull: feature extraction layers Type: general – SubjectFull: ablation testing Type: general Titles: – TitleFull: Detection: A convolutional network method for plant disease recognition Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Albert, Enow Takang Achuo – PersonEntity: Name: NameFull: Bille, Ngalle – PersonEntity: Name: NameFull: Leonard, Ngonkeu Mangaptche Eddy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |