Department of Biosystems Engineering, Chungnam National University, Daejeon 34134, Korea.
Department of Life Resources Industry, Dong-A University, Busan 49315, Korea.
Sensors (Basel). 2021 Jun 25;21(13):4363. doi: 10.3390/s21134363.
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
近年来，植物表型组学发展迅速。这一进展归因于新技术的不断创新和可用性，这些技术可以实现复杂植物性状的高通量表型分析。近年来，人工智能在科学的各个领域的应用也呈指数级增长。值得注意的是，人工智能的计算机视觉、机器学习和深度学习方面已成功集成到非侵入性成像技术中。这种集成正在通过机器和深度学习在稳健图像分析中的应用，逐步提高数据收集和分析的效率。此外，人工智能还促进了用于数据收集和管理的田间表型学软件和工具的发展。其中包括开源设备和工具，这些工具正在促进社区驱动的研究和数据共享，从而提供准确研究表型所需的大量数据。本文回顾了 2010 年至 2020 年间发表的 100 多篇关于人工智能在植物表型分析中应用的最新论文。它概述了当前的表型分析技术以及人工智能在植物表型分析中的持续集成。最后，讨论了当前方法/方法的局限性和未来的发展方向。
