This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: New research finds there is no substitute for real-world observations, but that—under the right circumstances—AI-generated images can be used to improve the performance of computer models used for species identification and biodiversity monitoring. In the study, North Carolina State University researchers investigated whether AI-generated, or "synthetic," images of 20 common North American trees could help train image-recognition models when there aren't enough real images available.
The researchers combined real images of trees from iNaturalist and the Auto Arborist Dataset with AI-generated images, then fed different combinations into a standard image-recognition model to see whether the synthetic images improved its ability to identify trees. The research is published in the journal Remote Sensing in Ecology and Conservation. Synthetic images improved the model's ability to recognize trees when real images were limited, suggesting that AI-generated images could help supplement real-world data when photographs are scarce.
"The greatest potential for this technology may be in studying species and places we know relatively little about," said the study's lead author Thomas Lake, a research scholar in the Center for Geospatial Analytics at NC State. Computer models require large amounts of data—which, in conservation, means images of plants, animals and other organisms—to accurately identify species for research and monitoring. But collecting enough real-world images can be difficult.
While community scientists have helped address this challenge by contributing photos through platforms such as iNaturalist, gaps remain in the availability of images for some species, particularly those that are less frequently photographed. "Some species are photographed thousands of times, while others may be rare, occur in remote places or simply receive much less attention. Even for common species, photographs may only come from certain places, seasons or viewpoints," said the study's co-author Chris Jones, a senior research scholar in the Center for Geospatial Analytics at NC State.
AI-generated images could be used to expand a small collection of real-world images, helping scientists build initial models for species identification and monitoring in areas where observations are limited while they continue collecting real images. Lake said it's important that the potential of AI-generated images for these kinds of conservation efforts not be overstated. In the study, he and his collaborators found that the synthetic images were less effective than real images overall in training the image-recognition model.
"Most of the AI-generated images looked plausible at first glance," Lake said. "But really, they didn't capture the fine details and variations that exist in the real world, telling us that these images can look realistic while still missing subtle information that matters in practice." That's why community science remains essential to data-driven conservation efforts, Lake said. Real images contributed by everyday people provide that missing information.
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