Review: Application of Artificial Intelligence in Phenomics

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ID: 269000
2021
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Abstract
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.
Reference Key
nabwire2021sensorsreview: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shona Nabwire;Hyun-Kwon Suh;Moon S. Kim;Insuck Baek;Byoung-Kwan Cho;Nabwire, Shona;Suh, Hyun-Kwon;Kim, Moon S.;Baek, Insuck;Cho, Byoung-Kwan;
Journal sensors
Year 2021
DOI
10.3390/s21134363
URL
https://doi.org/10.3390/s21134363
Keywords
Deep learning artificial intelligence plant phenomics field phenotyping high throughput phenotyping image-based phenotyping

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