从图像中自动识别 Aotearoa 的物种.
Hongyu Wang1, Paul Schlumbom2, Eibe Frank1
1School of Computing and Mathematical Sciences, University of Waikato, Hamilton, New Zealand.
Journal of the Royal Society of New Zealand
|August 4, 2025
概括
我们在新西兰开发了用于自动物种识别的机器学习模型. 这些人工智能工具的准确率超过76%,有助于保护和教育工作.
科学领域:
- 生态生态学 生态生态学
- 计算机科学 计算机科学
- 生物信息学是一种生物信息学.
背景情况:
- 准确的物种识别对于保护和教育至关重要.
- 新西兰独特的生物多样性为自动识别系统提供了机会.
- 机器学习为图像分类任务提供了强大的方法.
研究的目的:
- 为新西兰物种开发和评估基于神经网络的图像分类模型.
- 为了在移动设备上实现准确的离线物种识别.
- 为生物多样性研究和公众参与提供开源工具.
主要方法:
- 利用了来自iNaturalist的14,991个物种的数据集,涵盖动物,植物,真菌和其他王国.
- 训练有素的神经网络模型用于图像分类.
- 校准模型信心使用温度缩放和使用输入归因用于可解释性.
主要成果:
- 在所有物种中实现了超过76%的分类准确度.
- 为信心评估生成校准类概率估计.
- 证明了输入归因对于理解模型预测的实用性.
结论:
- 开发的模型为新西兰生物提供了准确可靠的物种识别.
- 这些开源,离线应用程序支持保护,教育和研究.
- 这项工作促进了生物多样性监测和公众与奥特罗亚独特物种的接触.
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