A Deep Learning Model Compression and Ensemble Approach for Weed Detection

dc.contributor.author Ofori, Martinson
dc.contributor.author El-Gayar, Omar
dc.contributor.author O'Brien, Austin
dc.contributor.author Noteboom, Cherie
dc.date.accessioned 2021-12-24T17:26:28Z
dc.date.available 2021-12-24T17:26:28Z
dc.date.issued 2022-01-04
dc.description.abstract Site-specific weed management is an important practice in precision agriculture. Current advances in artificial intelligence have resulted in the use of large deep convolutional neural networks for weed detection. In this paper, a transfer learning, model compression, and ensemble learning approach is introduced that is suitable for resource-limited hardware such as mobile and embedded devices. The resulting ensemble model achieves 91.2% classification accuracy which is comparable to the performance of state-of-the-art deep learning models (such as the vanilla VGG16, DenseNet, and ResNet) while being about 62.22% smaller in size than DenseNet (the smallest-sized full-sized model). The approach used in this study is beneficial for further development of deep convolutional neural networks on smaller resource-limited hardware typically used in agriculture, as well as other industries such as healthcare and telecommunication.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.138
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79470
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Analytics and Decision Support for Green IS and Sustainability Applications
dc.subject deep convolutional neural networks
dc.subject deep learning
dc.subject model compression
dc.subject precision agriculture
dc.subject weed detection
dc.title A Deep Learning Model Compression and Ensemble Approach for Weed Detection
dc.type.dcmi text
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