Y-Net: insect counting and segmentation using deep learning on embedded devices

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dc.contributor.authorKargar, Aminen
dc.contributor.authorZorbas, Dimitriosen
dc.contributor.authorGaffney, Michaelen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderDepartment of Agriculture, Food and the Marine, Irelanden
dc.contributor.funderTeagascen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-01-09T16:31:40Z
dc.date.available2025-01-09T16:31:40Z
dc.date.issued2024en
dc.description.abstractInsect pests can pose a serious threat to food production and agriculture in general and can cause substantial crop damage and economic losses. Monitoring insect pest populations is essential to control and mitigate these losses. Traditional monitoring methods are considered by growers and agronomists to be time-costly as well as labour-intensive tasks, which ultimately means that in times of high activity on farms it is a task which often is neglected. This study proposes an automated vision-based insect segmentation and counting approach through the use of deep learning (DL) models developed particularly for embedded systems. An image dataset for our target insect, Halyomorpha halys, was first created using images captured by our IoT-enabled image capture system deployed in a fruit orchard. Then, a Y-Net model inspired by UNet was developed with the capability of insect counting in addition to segmentation. The performance of this model was assessed using a variety of different metrics, and the results demonstrated the feasibility and effectiveness of the model in counting and segmentation of insects using Edge-AI algorithms capable of running on embedded systems. Based on the achieved results, the proposed Y-Net model achieved a Mean Squared Error (MSE) of 1.9 for the insect counting task, an Intersection over Union (IoU) of 84.5% and a Dice Similarity Coefficient (DSC) of 91.5% for the segmentation task, with an inference time of nearly 0.4 seconds on a smartphone.en
dc.description.sponsorshipDepartment of Agriculture, Food and the Marine, Ireland (Grant 2020 Trans National ERANET); Teagasc (Walsh Scholarship fund)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKargar, A., Zorbas, D., Gaffney, M., O’Flynn, B. and Tedesco, S. (2024) ‘Y-net: insect counting and segmentation using deep learning on embedded devices’, 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Glasgow, United Kingdom, 20-23 May 2024, pp. 1–6. https://doi.org/10.1109/I2MTC60896.2024.10561007en
dc.identifier.doihttps://doi.org/10.1109/I2MTC60896.2024.10561007en
dc.identifier.endpage6en
dc.identifier.urihttps://hdl.handle.net/10468/16800
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartof2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Glasgow, United Kingdom, 20-23 May 2024en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289_P2/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3835/IE/VistaMilk Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectImage segmentationen
dc.subjectObject countingen
dc.subjectCNN-based architectureen
dc.subjectInsect monitoringen
dc.subjectPrecision agricultureen
dc.subjectDeep learningen
dc.titleY-Net: insect counting and segmentation using deep learning on embedded devicesen
dc.typeArticle (peer-reviewed)en
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