Smartphone apps that identify plants from photographs can be as little as 4 per cent accurate, which could put people foraging for food at risk and also lead to endangered plants being mislabelled as weeds and eradicated.

从照片中识别植物的智能手机APP,其准确率只有4%，这可能会让寻找食物的人处于危险之中，也会导致濒危植物被错误地标记为杂草而被根除。

Julie Peacock at the University of Leeds, UK, and her colleagues evaluated six of the most popular apps: Google Lens, iNaturalist, Leaf Snap, Pl@ntNet, Plant Snap and Seek. They attempted to identify 38 species of plant in their natural habitat, at four locations in Ireland, with each app. The team found that some apps scored extremely poorly, while even the best fell short of 90 per cent accuracy.

英国利兹大学的朱莉·皮考克(Julie Peacock)和她的同事评估了六个最受欢迎的应用程序:Google Lens、iNaturalist、Leaf Snap、Pl@ntNet、Plant Snap和Seek。他们试着用每个应用程序识别38种自然栖息地植物，这些植物分布在爱尔兰的四个地点。该团队发现，一些应用程序的准确率非常低，而即使是最好的应用程序也达不到90%的准确率。

“There are lots of reasons why it’s important that either the apps are accurate, or people are aware that these apps are a guide but definitely not perfect,” says Peacock. For example, people could misidentify important native species as invasive, and remove them from their gardens, or consume potentially dangerous wild plants, thinking they are a harmless variety.

皮科克说:“知道这些应用程序是准确的，还是让人们意识到它们只是个不完美的辅助工具，非常重要，有很多原因可以解释。”例如，人们可能会错误地将重要的本地物种视为入侵物种，并将其从花园中移除，或者食用具有潜在危险的野生植物，把它们认为是无害的品种。

The apps use artificial intelligence algorithms trained on vast numbers of captioned photographs of plants. During training, the AI is taught to recognise not only the training photos, but also to spot similarities between them and new photographs, which allows them to identify plants.

这些应用程序使用人工智能算法，这些算法经过大量植物配图照片的训练。在训练过程中，人工智能不仅学会了识别训练照片，还能发现它们与新照片之间的相似之处，进而让它们能够识别植物。

Generally, the apps were all better at identifying flowers than leaves, which the researchers say is due to their greater variety of shape and colour providing the AI with more clues. But this wasn’t always the case. The iNaturalist app was able to correctly identify just 3.6 per cent of flowers and 6.8 per cent of leaves. Plant Snap identified 35.7 per cent of flowers correctly and 17.1 per cent of leaves. The highest accuracy was achieved by Pl@ntNet at 88.2 per cent.

理论上说，比起树叶，这些应用程序都更擅长识别花朵，研究人员表示，这是因为花朵的形状和颜色更丰富，为人工智能提供了更多线索。但现实情况并非总是如此。iNaturalist应用程序只能正确识别3.6%的花和6.8%的叶子。Plant Snap正确识别了35.7%的花朵和17.1%的叶子。准确率最高的是Pl@ntNet，达到88.2%。