{"title":"公民科学数据来源的一致性如何?一项使用免费自动图像识别应用程序进行木本植物识别的探索性研究","authors":"Kenneth A. Anyomi","doi":"10.1139/cjfr-2023-0203","DOIUrl":null,"url":null,"abstract":"Rapid advances in artificial intelligence have led to an upsurge in automated image recognition phone apps. This has increased public involvement in the collection, identification (ID) and analysis of biological data. While this is good for the field of biological data monitoring, it is not clear how consistent IDs are from different apps. The goal of this exploratory work is to verify the accuracy and consistency in plant species identification from two widely used and free apps i.e. PlantNet and iNaturalist app. This work was conducted by scanning leaf samples along Bruce trail in the Niagara Escarpment Biosphere reserve and the Royal Botanical Gardens arboretum, in Hamilton Ontario. Results show over 90% consistency in the identification of woody plants at the level of genus. At the species level, PlantNet app demonstrated 79% accuracy (i.e. 79 out of 100 species correctly identified) while the iNaturalist app demonstrated 44% accuracy. Enhancing species representation in the database for southern Ontario might help particularly species in the family Betulaceae, Rosaceae and Pinaceae. Complementary use of the apps is recommended as a cautionary measure to reduce the likelihood of error in species-level woody plant identification as well as using apps in conjunction with field guide.","PeriodicalId":9483,"journal":{"name":"Canadian Journal of Forest Research","volume":"26 1","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How consistent are citizen science data sources, an exploratory study using free automated image recognition apps for woody plant identification\",\"authors\":\"Kenneth A. Anyomi\",\"doi\":\"10.1139/cjfr-2023-0203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rapid advances in artificial intelligence have led to an upsurge in automated image recognition phone apps. This has increased public involvement in the collection, identification (ID) and analysis of biological data. While this is good for the field of biological data monitoring, it is not clear how consistent IDs are from different apps. The goal of this exploratory work is to verify the accuracy and consistency in plant species identification from two widely used and free apps i.e. PlantNet and iNaturalist app. This work was conducted by scanning leaf samples along Bruce trail in the Niagara Escarpment Biosphere reserve and the Royal Botanical Gardens arboretum, in Hamilton Ontario. Results show over 90% consistency in the identification of woody plants at the level of genus. At the species level, PlantNet app demonstrated 79% accuracy (i.e. 79 out of 100 species correctly identified) while the iNaturalist app demonstrated 44% accuracy. Enhancing species representation in the database for southern Ontario might help particularly species in the family Betulaceae, Rosaceae and Pinaceae. Complementary use of the apps is recommended as a cautionary measure to reduce the likelihood of error in species-level woody plant identification as well as using apps in conjunction with field guide.\",\"PeriodicalId\":9483,\"journal\":{\"name\":\"Canadian Journal of Forest Research\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Forest Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1139/cjfr-2023-0203\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Forest Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1139/cjfr-2023-0203","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}
How consistent are citizen science data sources, an exploratory study using free automated image recognition apps for woody plant identification
Rapid advances in artificial intelligence have led to an upsurge in automated image recognition phone apps. This has increased public involvement in the collection, identification (ID) and analysis of biological data. While this is good for the field of biological data monitoring, it is not clear how consistent IDs are from different apps. The goal of this exploratory work is to verify the accuracy and consistency in plant species identification from two widely used and free apps i.e. PlantNet and iNaturalist app. This work was conducted by scanning leaf samples along Bruce trail in the Niagara Escarpment Biosphere reserve and the Royal Botanical Gardens arboretum, in Hamilton Ontario. Results show over 90% consistency in the identification of woody plants at the level of genus. At the species level, PlantNet app demonstrated 79% accuracy (i.e. 79 out of 100 species correctly identified) while the iNaturalist app demonstrated 44% accuracy. Enhancing species representation in the database for southern Ontario might help particularly species in the family Betulaceae, Rosaceae and Pinaceae. Complementary use of the apps is recommended as a cautionary measure to reduce the likelihood of error in species-level woody plant identification as well as using apps in conjunction with field guide.
期刊介绍:
Published since 1971, the Canadian Journal of Forest Research is a monthly journal that features articles, reviews, notes and concept papers on a broad spectrum of forest sciences, including biometrics, conservation, disturbances, ecology, economics, entomology, genetics, hydrology, management, nutrient cycling, pathology, physiology, remote sensing, silviculture, social sciences, soils, stand dynamics, and wood science, all in relation to the understanding or management of ecosystem services. It also publishes special issues dedicated to a topic of current interest.