Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping
Date
Authors
Ganapathysubramanian, Baskar
Jones, Sarah
Ganapathysubramanian, Baskar
Sarkar, Soumik
Mueller, Daren
Sandhu, Kulbir
Nagasubramanian, Koushik
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Journal Issue
Series
Department
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.