在植物表型化中的可解释的深度学习
Sakib Mostafa1, Debajyoti Mondal1, Karim Panjvani2
1Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Frontiers in artificial intelligence
|October 5, 2023
概括
可解释的人工智能 (XAI) 可以帮助解释植物表型化中的深度学习模型. 这项技术提高了基于图像的作物数据的可靠性,这对于改善全球粮食安全和作物弹性至关重要.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物生物学 植物生物学
背景情况:
- 气候变化和人口增长威胁全球粮食安全.
- 植物表型化加速了作物育种和管理,但依赖于复杂的深度学习模型.
- 现型化中的深度学习模型通常是"黑子",缺乏可解释性.
研究的目的:
- 审查可解释AI (XAI) 在植物表型化中的应用.
- 突出XAI对理解基于图像的表型数据的好处.
- 鼓励将XAI纳入未来的植物科学研究.
主要方法:
- 关于植物表型和相关领域现有XAI研究的文献综述.
- 分析如何XAI可以阐明深度学习模型表示.
- 综合发现,以指导植物研究人员采用XAI.
主要成果:
- XAI提供了一种解释植物表型化中使用的深度学习模型的途径.
- 可解释模型增强了模型决策的解释和特征相关性.
- XAI可以提高农业基于图像的表型数据的可靠性.
结论:
- 通过使深度学习模型透明,XAI对于推进植物表型定型至关重要.
- 整合XAI将赋予育种者和种植者可靠的作物改进数据.
- XAI促进了对人工智能驱动的可持续粮食生产见解的信任.
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