Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy, Plant Pathology and Microbiology
Journal or Book Title
Trends in Plant Science
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Singh, Arti; Jones, Sarah; Ganapathysubramanian, Baskar; Sarkar, Soumik; Mueller, Daren S.; Sandhu, Kulbir; and Nagasubramanian, Koushik, "Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping" (2020). Mechanical Engineering Publications. 434.