Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

Date
2020-08-20
Authors
Singh, Arti
Jones, Sarah
Ganapathysubramanian, Baskar
Sarkar, Soumik
Mueller, Daren
Sandhu, Kulbir
Nagasubramanian, Koushik
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Altmetrics
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Mechanical Engineering
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Agronomy
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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.

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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.

Keywords
image-based phenotyping, machine learning, deep learning, biotic stress, abiotic stress, standard area diagram
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