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|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Smart food waste management : embedded machine learning vs cloud based solutions|
|Proceedings:||Proceedings of the 2nd FTAL Conference 2021 Sustainable Smart Cities and Regions|
|Editors of the parent work:||Mugellini, Elena|
|Conference details:||FTAL Conference 2021 – Sustainable smart cities and regions, Lugano, Switzerland, 28-29 October 2021|
|Publisher / Ed. Institution:||CEUR Workshop Proceedings|
|Subjects:||Food waste; IoT; Embedded machine learning; TensorRT; Embedded inference|
|Subject (DDC):||006: Special computer methods|
|Abstract:||In Switzerland, 2.8 million tons of food are lost or wasted across all stages of food production - every year. This equates to approximately 330 kg of food waste per person. By analysing and classifying discarded food with a smart waste analysis system combined with machine learning, valuable insights can be gained and the amount of wasted food can be significantly reduced. In this paper, we present how we have developed an embedded system which helps to solve this task. The embedded system operates in a decentralized manner: It captures an image every time food is thrown into a bin. The discarded food is identified and classified with machine learning algorithms. This provides a detailed insight into the structure of food waste for customers, e.g. restaurants or canteens. We implemented the machine learning algorithm directly on the embedded systems control unit. We found that running machine learning directly on embedded devices has many advantages compared to running them in the cloud: We saved significant amounts of cloud storage and reduced power consumption by up to a factor 100. In addition, privacy was increased and required bandwidth reduced because only the machine learning results are forwarded to the cloud, not the full data.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Embedded Systems (InES)|
|Published as part of the ZHAW project:||FWA: Visual Food Waste Analysis for Sustainable Kitchens|
|Appears in collections:||Publikationen School of Engineering|
Files in This Item:
|2021_Zingg-etal_Smart-food-waste-management_FTAL.pdf||2.69 MB||Adobe PDF|
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Zingg, R., Andermatt, P., Mazloumian, A., & Rosenthal, M. (2021). Smart food waste management : embedded machine learning vs cloud based solutions [Conference paper]. In E. Mugellini & E. Carpanzano (Eds.), Proceedings of the 2nd FTAL Conference 2021 Sustainable Smart Cities and Regions. CEUR Workshop Proceedings. https://doi.org/10.21256/zhaw-23847
Zingg, R. et al. (2021) ‘Smart food waste management : embedded machine learning vs cloud based solutions’, in E. Mugellini and E. Carpanzano (eds) Proceedings of the 2nd FTAL Conference 2021 Sustainable Smart Cities and Regions. CEUR Workshop Proceedings. Available at: https://doi.org/10.21256/zhaw-23847.
R. Zingg, P. Andermatt, A. Mazloumian, and M. Rosenthal, “Smart food waste management : embedded machine learning vs cloud based solutions,” in Proceedings of the 2nd FTAL Conference 2021 Sustainable Smart Cities and Regions, Oct. 2021. doi: 10.21256/zhaw-23847.
Zingg, Raphael, et al. “Smart Food Waste Management : Embedded Machine Learning Vs Cloud Based Solutions.” Proceedings of the 2nd FTAL Conference 2021 Sustainable Smart Cities and Regions, edited by Elena Mugellini and Emanuele Carpanzano, CEUR Workshop Proceedings, 2021, https://doi.org/10.21256/zhaw-23847.
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