{"title":"MCFF:基于多尺度CNN特征融合的植物叶片检测","authors":"Ying Li, Zhaohong Huang, Yang Sun","doi":"10.1109/ITME53901.2021.00058","DOIUrl":null,"url":null,"abstract":"Plant leaf detection is one of the essential aspects of the scientific plant breeding and precision agriculture process. Manual detection requires professional knowledge of the operators, high labor costs, and long time-consuming cycles. To this end, this paper proposes a multi-scale CNN feature fusion (MCFF) to detect the Rosette plant, Arabidopsis, and Tobacco. The experimental results indicate that the mean average precision of the proposed method is higher than the traditional methods such as RetinaNet, CenterNet, and Faster R-CNN.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"25 1","pages":"246-250"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"MCFF: Plant leaf detection based on multi-scale CNN feature fusion\",\"authors\":\"Ying Li, Zhaohong Huang, Yang Sun\",\"doi\":\"10.1109/ITME53901.2021.00058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Plant leaf detection is one of the essential aspects of the scientific plant breeding and precision agriculture process. Manual detection requires professional knowledge of the operators, high labor costs, and long time-consuming cycles. To this end, this paper proposes a multi-scale CNN feature fusion (MCFF) to detect the Rosette plant, Arabidopsis, and Tobacco. The experimental results indicate that the mean average precision of the proposed method is higher than the traditional methods such as RetinaNet, CenterNet, and Faster R-CNN.\",\"PeriodicalId\":6774,\"journal\":{\"name\":\"2021 11th International Conference on Information Technology in Medicine and Education (ITME)\",\"volume\":\"25 1\",\"pages\":\"246-250\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 11th International Conference on Information Technology in Medicine and Education (ITME)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITME53901.2021.00058\",\"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 11th International Conference on Information Technology in Medicine and Education (ITME)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITME53901.2021.00058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MCFF: Plant leaf detection based on multi-scale CNN feature fusion
Plant leaf detection is one of the essential aspects of the scientific plant breeding and precision agriculture process. Manual detection requires professional knowledge of the operators, high labor costs, and long time-consuming cycles. To this end, this paper proposes a multi-scale CNN feature fusion (MCFF) to detect the Rosette plant, Arabidopsis, and Tobacco. The experimental results indicate that the mean average precision of the proposed method is higher than the traditional methods such as RetinaNet, CenterNet, and Faster R-CNN.