{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T16:50:07Z","timestamp":1777999807465,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Robotics Programme, the Agency for Science, Technology and Research","award":["Funding Agency Project No. 192 25 00051, 192 22 00058"],"award-info":[{"award-number":["Funding Agency Project No. 192 25 00051, 192 22 00058"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.<\/jats:p>","DOI":"10.3390\/s20185280","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T10:24:09Z","timestamp":1600165449000},"page":"5280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-9814","authenticated-orcid":false,"given":"Balakrishnan","family":"Ramalingam","sequence":"first","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Rajesh Elara","family":"Mohan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Sathian","family":"Pookkuttath","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Braulio F\u00e9lix","family":"G\u00f3mez","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Charan Satya Chandra","family":"Sairam Borusu","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Tey","family":"Wee Teng","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore"}]},{"given":"Yokhesh Krishnasamy","family":"Tamilselvam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Western Ontario, London, ON N6A 3K7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"ref_1","unstructured":"(2020, July 16). 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