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We present a low-cost device with high power autonomy and an adequate picture quality that reports an internal image of the trap to a server and counts the insects it contains based on quantized and embedded deep-learning models. The paper compares different aspects of performance of three different edge devices, namely ESP32, Raspberry Pi Model 4 (RPi), and Google Coral, running a deep learning framework (TensorFlow Lite). All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption. Our findings suggest that ESP32 appears to be the best choice in the context of this application according to our policy for low-cost devices.<\/jats:p>","DOI":"10.3390\/s22052006","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"2006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities"],"prefix":"10.3390","volume":"22","author":[{"given":"Ioannis","family":"Saradopoulos","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece"}]},{"given":"Ilyas","family":"Potamitis","sequence":"additional","affiliation":[{"name":"Department of Music Technology and Acoustics, Hellenic Mediterranean University, 74100 Rethymno, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3482-9215","authenticated-orcid":false,"given":"Stavros","family":"Ntalampiras","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Milan, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1052-1948","authenticated-orcid":false,"given":"Antonios I.","family":"Konstantaras","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece"}]},{"given":"Emmanuel N.","family":"Antonidakis","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2002545117","DOI":"10.1073\/pnas.2002545117","article-title":"Deep learning and computer vision will transform entomology","volume":"118","author":"Bjerge","year":"2021","journal-title":"Proc. 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