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First published in Ecological Informatics, v92 (Dec 2025) published by Elsevier BV. DOI: https://doi.org/10.1016/j.ecoinf.2025.103437

Document Type

Article

Publication Version

Published Version

Keywords

Deep learning, Object detection, Pruning, Quantization, Tick and mosquito citizen science

Journal/Book/Conference Title

Ecological Informatics

Volume

92

First Page

1

Last Page

12

Abstract

Citizen science has emerged as an effective approach for infectious disease surveillance. With advancements in machine learning, entomologists can now be relieved from the labor-intensive task of species identification. However, deploying machine learning models on mobile devices presents challenges due to constraints on battery life and memory capacity. In this study, we explore the potential of various model compression techniques for deploying machine learning models on resource-limited devices, enabling low-energy consumption and on-device processing for disease surveillance in remote or low-resource settings. We compared two main-stream model compression techniques, pruning and quantization on various mobile devices. Our findings indicate that quantization methods outperform pruning methods in terms of efficiency. Furthermore, we propose to integrate structured and unstructured pruning to enhance model performance while addressing key constraints of mobile deployment.

Department

Department of Computer Science

Original Publication Date

10-8-2025

Object Description

1 PDF File

DOI of published version

10.1016/j.ecoinf.2025.103437

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Copyright

©2025 The Author(s)

Language

en

File Format

application/pdf

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