Nakhle Farid, Harfouche Antoine L
Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy.
Patterns (N Y). 2021 Sep 10;2(9):100323. doi: 10.1016/j.patter.2021.100323.
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
基于高通量图像的技术如今在快速发展的数字表型组学领域得到广泛应用,并产生了数量不断增加且种类日益多样的数据。人工智能(AI)正在成为将海量数据转化为有价值的预测和见解的游戏规则改变者。然而,这需要专业的编程技能以及对机器学习、深度学习和集成学习算法的深入理解。在此,我们试图系统地回顾表型组学数据社区可用的不同工具、技术和服务的使用情况,并展示它们如何应用于基于可解释人工智能的图像分析中的特定问题。本教程为新手和专家提供了实用且有用的资源,以便在基于可解释人工智能的育种计划中挖掘表型组学数据的潜力。