Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

作者：Arti Singh, Sarah E. Jones, Baskar Ganapathysubramanian, Soumik Sarkar, Daren S. Mueller, Kulbir Singh Sandhu, Koushik Nagasubramanian · 发表于：Trends in Plant Science · 年份：2020 · DOI：10.1016/j.tplants.2020.07.010 · 被引用次数：229 · 研究领域：Smart Agriculture and AI、Plant Pathogens and Fungal Diseases、Leaf Properties and Growth Measurement

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.