A Cloud-hosted web app utilizing a hybrid approach for insect and mold classification.
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| Title: | A Cloud-hosted web app utilizing a hybrid approach for insect and mold classification. |
|---|---|
| Authors: | Raghuwanshi, Pranshu1 (AUTHOR) pranshuraghu7@gmail.com, Kaushik, Rekha2 (AUTHOR) rekhakaushik@iiitbhopal.ac.in |
| Source: | Multimedia Tools & Applications. Dec2025, Vol. 84 Issue 41, p49337-49353. 17p. |
| Subjects: | Classification of insects, Fungi classification, Cloud computing, Web-based user interfaces, Amazon Web Services Inc., Computer systems, Image recognition (Computer vision), Automatic classification, Research methodology |
| Abstract: | Insects and molds pose a serious threat to stored products, particularly grains. Under favourable conditions, these infestations can proliferate rapidly, and late discovery can lead to contamination, affecting both the quality and quantity of the products. To address this issue, several automated systems have been proposed, but they often face challenges such as insufficient data, sensitivity to noise and harsh environments, and feasibility for real-world application. Therefore, there is significant scope for improvement in making these applications more precise, robust, and accessible to users. This study proposes a web application implementing a hybrid approach that combines audio and image classification models to provide accurate and reliable real-time classification predictions via a cloud-hosted web application. A CNN was constructed for the audio classification of insects, and a YOLOv8 classification model was implemented for insect and mold images. The predictions from these models were combined using the concept of late fusion, and the performance of this combined approach was compared with the individual modalities. Finally, the proposed approach was integrated into a Flask web application, deployed using Docker on an AWS EC2 instance, to provide users with an accessible platform for early-stage detection and classification. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 190467390 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Cloud-hosted web app utilizing a hybrid approach for insect and mold classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Raghuwanshi%2C+Pranshu%22">Raghuwanshi, Pranshu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pranshuraghu7@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kaushik%2C+Rekha%22">Kaushik, Rekha</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> rekhakaushik@iiitbhopal.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Dec2025, Vol. 84 Issue 41, p49337-49353. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Classification+of+insects%22">Classification of insects</searchLink><br /><searchLink fieldCode="DE" term="%22Fungi+classification%22">Fungi classification</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Amazon+Web+Services+Inc%2E%22">Amazon Web Services Inc.</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+classification%22">Automatic classification</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Insects and molds pose a serious threat to stored products, particularly grains. Under favourable conditions, these infestations can proliferate rapidly, and late discovery can lead to contamination, affecting both the quality and quantity of the products. To address this issue, several automated systems have been proposed, but they often face challenges such as insufficient data, sensitivity to noise and harsh environments, and feasibility for real-world application. Therefore, there is significant scope for improvement in making these applications more precise, robust, and accessible to users. This study proposes a web application implementing a hybrid approach that combines audio and image classification models to provide accurate and reliable real-time classification predictions via a cloud-hosted web application. A CNN was constructed for the audio classification of insects, and a YOLOv8 classification model was implemented for insect and mold images. The predictions from these models were combined using the concept of late fusion, and the performance of this combined approach was compared with the individual modalities. Finally, the proposed approach was integrated into a Flask web application, deployed using Docker on an AWS EC2 instance, to provide users with an accessible platform for early-stage detection and classification. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190467390 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-025-21090-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 49337 Subjects: – SubjectFull: Classification of insects Type: general – SubjectFull: Fungi classification Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Web-based user interfaces Type: general – SubjectFull: Amazon Web Services Inc. Type: general – SubjectFull: Computer systems Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Automatic classification Type: general – SubjectFull: Research methodology Type: general Titles: – TitleFull: A Cloud-hosted web app utilizing a hybrid approach for insect and mold classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Raghuwanshi, Pranshu – PersonEntity: Name: NameFull: Kaushik, Rekha IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 41 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
| ResultId | 1 |
