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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #384404
Research Project: Sensing Technologies for the Detection and Characterization of Microbial, Chemical, and Biological Contaminants in Foods

Location: Environmental Microbial & Food Safety Laboratory

Title: Review: Application of artificial intelligence in phenomics

Author
item NABWIRE, SHONA - Chungnam National University
item SUH, HYUN - Dong-A University
item Kim, Moon
item BAEK, INSUCK - Orise Fellow
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Sensors
Publication Type: Review Article
Publication Acceptance Date: 6/22/2021
Publication Date: 6/25/2021
Citation: Nabwire, S., Suh, H., Kim, M.S., Baek, I., Cho, B. 2021. Review: Application of artificial intelligence in phenomics. Sensors. 21, 4363. https://doi.org/10.3390/s21134363.
DOI: https://doi.org/10.3390/s21134363

Interpretive Summary: Advancement of spectral imaging and machine vision technologies has enabled high-throughput phenotyping of complex plant traits over the past decade. The computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive image-based phenotyping. In this review paper, an overview of current phenotyping technologies and the ongoing integration of artificial intelligence in plant phenotyping is presented. The information provided in this paper is useful to scientists and engineers who are interested in developing machine vision-based phenotyping applications.

Technical 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 to enable high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has 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 image-based phenotyping. This integration is gradually improving the efficiency of data collection and analysis and has fostered further research into the utilization of artificial intelligence methods in field phenotyping and phenotype data management. This paper extensively reviews 90+ current state-of-the-art papers of AI-applied plant phenotyping published between 2010 to 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence in plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.