专家,新手和AI对森林根图像注释的变化
Grace Handy1,2, Imogen Carter3,4, A Rob Mackenzie3,4
1Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK. gih849@student.bham.ac.uk.
Plant methods
|October 1, 2024
概括
手动的根注释显示了很高的变化. 一个可访问的人工智能 (AI) 模型,一个卷积神经网络 (CNN),尽管处理速度更快,但在复杂的森林土壤图像中无法准确测量根长度.
科学领域:
- 生态生态学 生态生态学
- 植物科学 植物科学
- 土壤科学 土壤科学
背景情况:
- 手动根动力学研究是时间密集的,并受到注释者偏差的影响.
- 人工智能 (AI) 工具对图像分析有希望,但需要在异质的非农业土壤中进行测试.
- 森林根动力学对于理解全球碳循环至关重要.
研究的目的:
- 在根长度测量中量化人类注释器变异.
- 在森林土壤图像中评估卷积神经网络 (CNN) 用于根长度分析.
- 在复杂,异构的环境中评估AI模型的性能.
主要方法:
- 收集了来自一个成熟的薄叶温带森林的微观图像.
- 在具有不同经验水平的人类注释者中评估了根长度注释的变化.
- 将受过在可访问软件上训练的CNN模型应用于图像数据集.
主要成果:
- 人类注释者经验显著影响了根长度测量.
- 与专家手册注释相比,CNN模型高估了根长度 (p=0.01).
- 美国有线电视新闻网的根长度变化估计比人工更接近,但仍然显示了变化.
结论:
- 手动根注释是主观的,取决于个体.
- 经过测试的CNN模型缺乏在复杂森林土壤中的生态应用的准确性和精度.
- 对自然生态系统的可访问CNN的进一步开发和评估是必要的.
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