{"title":"AlertTrap:边缘计算远程昆虫监测系统的设计","authors":"Duy A. Pham, A. D. Le, Dong T. Pham, H. B. Vo","doi":"10.1109/NICS54270.2021.9701558","DOIUrl":null,"url":null,"abstract":"Fruit flies become one of the most worrisome insect species to fruit yields. AlertTrap proposes and tests the constituent components to construct an efficient autonomous trap which sends notification to farmers when the number of flies exceeds a predefined threshold. The trap is powered with solar panels, equipped with a Lynfield-inspired sticky trap that is optimized to be attractive to fruit flies and controlled by an Arduino Board to collect data and circulate the energy through the system. The fruit flies are then counted on a Raspberry Pi Board by YOLOv4-tiny and SSD-MobileNet object detection algorithms with over 95% average precision at IoU threshold of 0.5 and an alert signal is sent to the farmers based on the number of fruit flies in the trap.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"AlertTrap: On Designing An Edge-Computing Remote Insect Monitoring System\",\"authors\":\"Duy A. Pham, A. D. Le, Dong T. Pham, H. B. Vo\",\"doi\":\"10.1109/NICS54270.2021.9701558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fruit flies become one of the most worrisome insect species to fruit yields. AlertTrap proposes and tests the constituent components to construct an efficient autonomous trap which sends notification to farmers when the number of flies exceeds a predefined threshold. The trap is powered with solar panels, equipped with a Lynfield-inspired sticky trap that is optimized to be attractive to fruit flies and controlled by an Arduino Board to collect data and circulate the energy through the system. The fruit flies are then counted on a Raspberry Pi Board by YOLOv4-tiny and SSD-MobileNet object detection algorithms with over 95% average precision at IoU threshold of 0.5 and an alert signal is sent to the farmers based on the number of fruit flies in the trap.\",\"PeriodicalId\":296963,\"journal\":{\"name\":\"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NICS54270.2021.9701558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NICS54270.2021.9701558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AlertTrap: On Designing An Edge-Computing Remote Insect Monitoring System
Fruit flies become one of the most worrisome insect species to fruit yields. AlertTrap proposes and tests the constituent components to construct an efficient autonomous trap which sends notification to farmers when the number of flies exceeds a predefined threshold. The trap is powered with solar panels, equipped with a Lynfield-inspired sticky trap that is optimized to be attractive to fruit flies and controlled by an Arduino Board to collect data and circulate the energy through the system. The fruit flies are then counted on a Raspberry Pi Board by YOLOv4-tiny and SSD-MobileNet object detection algorithms with over 95% average precision at IoU threshold of 0.5 and an alert signal is sent to the farmers based on the number of fruit flies in the trap.