Development of durian leaf disease detection on Android device.
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| Title: | Development of durian leaf disease detection on Android device. |
|---|---|
| Authors: | Sabarre, A. L.1, Navidad, A. S.2, Torbela, D. S.1, Adtoon, J. J.1 Jetron.adtoon22@gmail.com |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Dec2021, Vol. 11 Issue 6, p4962-4971. 10p. |
| Subjects: | Android (Operating system), Leaf development, Durian, Plant diseases, Mobile apps, Agricultural industries |
| Geographic Terms: | Philippines |
| Abstract: | Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent's objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 151460810 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of durian leaf disease detection on Android device. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sabarre%2C+A%2E+L%2E%22">Sabarre, A. L.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Navidad%2C+A%2E+S%2E%22">Navidad, A. S.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Torbela%2C+D%2E+S%2E%22">Torbela, D. S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Adtoon%2C+J%2E+J%2E%22">Adtoon, J. J.</searchLink><relatesTo>1</relatesTo><i> Jetron.adtoon22@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Dec2021, Vol. 11 Issue 6, p4962-4971. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Leaf+development%22">Leaf development</searchLink><br /><searchLink fieldCode="DE" term="%22Durian%22">Durian</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+diseases%22">Plant diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+apps%22">Mobile apps</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+industries%22">Agricultural industries</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Philippines%22">Philippines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent's objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v11i6.pp4962-4971 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 4962 Subjects: – SubjectFull: Android (Operating system) Type: general – SubjectFull: Leaf development Type: general – SubjectFull: Durian Type: general – SubjectFull: Plant diseases Type: general – SubjectFull: Mobile apps Type: general – SubjectFull: Agricultural industries Type: general – SubjectFull: Philippines Type: general Titles: – TitleFull: Development of durian leaf disease detection on Android device. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sabarre, A. L. – PersonEntity: Name: NameFull: Navidad, A. S. – PersonEntity: Name: NameFull: Torbela, D. S. – PersonEntity: Name: NameFull: Adtoon, J. J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 11 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
| ResultId | 1 |
