Campus Units

Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy, Plant Pathology and Microbiology

Document Type

Article

Publication Version

Published Version

Publication Date

8-20-2020

Journal or Book Title

Trends in Plant Science

DOI

10.1016/j.tplants.2020.07.010

Abstract

Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments.

Comments

This article is published as Singh, Arti, Sarah Jones, Baskar Ganapathysubramanian, Soumik Sarkar, Daren Mueller, Kulbir Sandhu, and Koushik Nagasubramanian. "Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping." Trends in Plant Science (2020). DOI: 10.1016/j.tplants.2020.07.010. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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