Jones Hamlyn G
Division of Plant Sciences, School of Life Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee, UK.
School of Agriculture and Environment, University of Western Australia, Perth, WA, Australia.
AoB Plants. 2020 Sep 23;12(6):plaa052. doi: 10.1093/aobpla/plaa052. eCollection 2020 Dec.
There has been a recent explosion in development of image recognition technology and its application to automated plant identification, so it is timely to consider its potential for field botany. Nine free apps or websites for automated plant identification and suitable for use on mobile phones or tablet computers in the field were tested on a disparate set of 38 images of plants or parts of plants chosen from the higher plant flora of Britain and Ireland. There were large differences in performance with the best apps identifying >50 % of samples tested to genus or better. Although the accuracy is good for some of the top-rated apps, for any quantitative biodiversity study or for ecological surveys, there remains a need for validation by experts or against conventional floras. Nevertheless, the better-performing apps should be of great value to beginners and amateurs and may usefully stimulate interest in plant identification and nature. Potential uses of automated image recognition plant identification apps are discussed and recommendations made for their future use.
最近，图像识别技术及其在植物自动识别中的应用发展迅速，因此，探讨其在野外植物学中的潜力正逢其时。我们测试了九款免费的植物自动识别应用程序或网站，这些程序适合在野外使用手机或平板电脑操作。测试图像从英国和爱尔兰高等植物区系中选取，共38张，涵盖植物或植物部分，种类各异。不同应用程序的表现差异很大，表现最佳的应用程序能识别超过50%的测试样本至属一级或更精确。尽管一些排名靠前的应用程序准确率较高，但对于任何定量生物多样性研究或生态调查而言，仍需专家验证或对照传统植物志进行核实。尽管如此，性能较好的应用程序对初学者和业余爱好者具有很大价值，可能会有效地激发人们对植物识别和自然的兴趣。本文讨论了自动图像识别植物鉴定应用程序的潜在用途，并对其未来使用提出了建议。
