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Review: Application of Artificial Intelligence in Phenomics

Authors :
Shona Nabwire
Hyun-Kwon Suh
Moon S. Kim
Insuck Baek
Byoung-Kwan Cho
Source :
Sensors, Vol 21, Iss 13, p 4363 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

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.

Details

Language :
English
ISSN :
14248220
Volume :
21
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.48319e97ce5346e5b1bef34f21551fe4
Document Type :
article
Full Text :
https://doi.org/10.3390/s21134363