Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment
The deployment of machine learning models on mobile platforms has ushered in a new era of innovation across diverse sectors, including agriculture, where such applications hold immense promise for empowering farmers with cutting-edge technologies. In this context, the threat posed by insects to crop...
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2025
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| Online Access: | https://hdl.handle.net/11073/26040 |
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| _version_ | 1864513435819048960 |
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| author | Akhtar, Muhammad Hannan |
| author2 | Eksheir, Ibrahim Shanableh, Tamer |
| author2_role | author author |
| author_facet | Akhtar, Muhammad Hannan Eksheir, Ibrahim Shanableh, Tamer |
| author_role | author |
| dc.creator.none.fl_str_mv | Akhtar, Muhammad Hannan Eksheir, Ibrahim Shanableh, Tamer |
| dc.date.none.fl_str_mv | 2025-05-01T08:44:13Z 2025-05-01T08:44:13Z 2025-04-25 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Akhtar,M.H.;Eksheir,I.; Shanableh, T. Edge-OptimizedDeep Learning Architectures for Classification of Agricultural Insects withMobileDeployment. Information 2025, 16, 348. https://doi.org/10.3390/info16050348 2078-2489 https://hdl.handle.net/11073/26040 10.3390/info16050348 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/info16050348 |
| dc.rights.none.fl_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.subject.none.fl_str_mv | On-edge classification Model quantization TensorFlow Lite Insect classification EfficientNet Deep learning |
| dc.title.none.fl_str_mv | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| dc.type.none.fl_str_mv | Published version Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The deployment of machine learning models on mobile platforms has ushered in a new era of innovation across diverse sectors, including agriculture, where such applications hold immense promise for empowering farmers with cutting-edge technologies. In this context, the threat posed by insects to crop yields during harvest has escalated, fueled by factors such as evolution and climate change-induced shifts in insect behavior. To address this challenge, smart insect monitoring systems and detection models have emerged as crucial tools for farmers and IoT-based systems, enabling interventions to safeguard crops. The primary contribution of this study lies in its systematic investigation of model optimization techniques for edge deployment, including Post-Training Quantization, Quantization-Aware Training, and Data Representative Quantization. As such, we address the crucial need for efficient, on-site pest detection tools in agricultural settings. We provide a detailed analysis of the trade-offs between model size, inference speed, and accuracy across different optimization approaches, ensuring practical applicability in resource-constrained farming environments. Our study explores various methodologies for model development, including the utilization of Mobile-ViT and EfficientNet architectures, coupled with transfer learning and fine-tuning techniques. Using the Dangerous Farm Insects Dataset, we achieve an accuracy of 82.6% and 77.8% on validation and test datasets, respectively, showcasing the efficacy of our approach. Furthermore, we investigate quantization techniques to optimize model performance for on-device inference, ensuring seamless deployment on mobile devices and other edge devices without compromising accuracy. The best quantized model, produced through Post-Training Quantization, was able to maintain a classification accuracy of 77.8% while significantly reducing the model size from 33 MB to 9.6 MB. To validate the generalizability of our solution, we extended our experiments to the larger IP102 dataset. The quantized model produced using Post-Training Quantization was able to maintain a classification accuracy of 59.6% while also reducing the model size from 33 MB to 9.6 MB, thus demonstrating that our solution maintains a competitive performance across a broader range of insect classes. |
| format | article |
| id | aus_0d8e4cb4cd3890f1c19cec906e50b693 |
| identifier_str_mv | Akhtar,M.H.;Eksheir,I.; Shanableh, T. Edge-OptimizedDeep Learning Architectures for Classification of Agricultural Insects withMobileDeployment. Information 2025, 16, 348. https://doi.org/10.3390/info16050348 2078-2489 10.3390/info16050348 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26040 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| spelling | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile DeploymentAkhtar, Muhammad HannanEksheir, IbrahimShanableh, TamerOn-edge classificationModel quantizationTensorFlow LiteInsect classificationEfficientNetDeep learningThe deployment of machine learning models on mobile platforms has ushered in a new era of innovation across diverse sectors, including agriculture, where such applications hold immense promise for empowering farmers with cutting-edge technologies. In this context, the threat posed by insects to crop yields during harvest has escalated, fueled by factors such as evolution and climate change-induced shifts in insect behavior. To address this challenge, smart insect monitoring systems and detection models have emerged as crucial tools for farmers and IoT-based systems, enabling interventions to safeguard crops. The primary contribution of this study lies in its systematic investigation of model optimization techniques for edge deployment, including Post-Training Quantization, Quantization-Aware Training, and Data Representative Quantization. As such, we address the crucial need for efficient, on-site pest detection tools in agricultural settings. We provide a detailed analysis of the trade-offs between model size, inference speed, and accuracy across different optimization approaches, ensuring practical applicability in resource-constrained farming environments. Our study explores various methodologies for model development, including the utilization of Mobile-ViT and EfficientNet architectures, coupled with transfer learning and fine-tuning techniques. Using the Dangerous Farm Insects Dataset, we achieve an accuracy of 82.6% and 77.8% on validation and test datasets, respectively, showcasing the efficacy of our approach. Furthermore, we investigate quantization techniques to optimize model performance for on-device inference, ensuring seamless deployment on mobile devices and other edge devices without compromising accuracy. The best quantized model, produced through Post-Training Quantization, was able to maintain a classification accuracy of 77.8% while significantly reducing the model size from 33 MB to 9.6 MB. To validate the generalizability of our solution, we extended our experiments to the larger IP102 dataset. The quantized model produced using Post-Training Quantization was able to maintain a classification accuracy of 59.6% while also reducing the model size from 33 MB to 9.6 MB, thus demonstrating that our solution maintains a competitive performance across a broader range of insect classes.College of EngineeringDepartment of Computer Science and EngineeringMDPI2025-05-01T08:44:13Z2025-05-01T08:44:13Z2025-04-25Published versionPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAkhtar,M.H.;Eksheir,I.; Shanableh, T. Edge-OptimizedDeep Learning Architectures for Classification of Agricultural Insects withMobileDeployment. Information 2025, 16, 348. https://doi.org/10.3390/info160503482078-2489https://hdl.handle.net/11073/2604010.3390/info16050348en_UShttps://doi.org/10.3390/info16050348Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/oai:repository.aus.edu:11073/260402025-05-01T14:40:57Z |
| spellingShingle | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment Akhtar, Muhammad Hannan On-edge classification Model quantization TensorFlow Lite Insect classification EfficientNet Deep learning |
| status_str | publishedVersion |
| title | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| title_full | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| title_fullStr | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| title_full_unstemmed | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| title_short | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| title_sort | Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment |
| topic | On-edge classification Model quantization TensorFlow Lite Insect classification EfficientNet Deep learning |
| url | https://hdl.handle.net/11073/26040 |