Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping.

Journal: Trends in plant science
Published Date: Aug 20, 2020

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.

Authors

  • Arti Singh
    Department of Agronomy, Iowa State University, Ames, IA, USA. Electronic address: arti@iastate.edu.
  • Sarah Jones
    Department of Agronomy, Iowa State University, Ames, IA, USA.
  • Baskar Ganapathysubramanian
    Department of Mechanical Engineering and Translational AI Research and Education Center, Iowa State University, Ames, Iowa 50011, United States.
  • Soumik Sarkar
    Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
  • Daren Mueller
    Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA.
  • Kulbir Sandhu
    Department of Agronomy, Iowa State University, Ames, IA, USA.
  • Koushik Nagasubramanian
    Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA.

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