Machine Learning for High-Throughput Stress Phenotyping in Plants.

Area of Science:

• Plant Science
• Computational Biology
• Agricultural Science

Background:

• High-throughput imaging generates vast plant data, necessitating advanced analytical methods.
• Machine learning (ML) is crucial for extracting meaningful patterns and features for plant stress phenotyping.
• Current ML applications in plant stress research lack a unified framework.

Purpose of the Study:

• To provide a comprehensive overview and taxonomy of ML tools for plant stress phenotyping.
• To guide the plant community in applying appropriate ML tools and best practices.
• To facilitate data assimilation and feature identification for biotic and abiotic stress traits.

Main Methods:

• Categorization of ML approaches across four decision stages: identification, classification, quantification, and prediction (ICQP).
• Development of a user-friendly taxonomy for ML tool selection.
• Review of ML best-practice guidelines for plant stress trait analysis.

Main Results:

• A structured framework detailing ML tool deployment in plant stress phenotyping.
• Identification of specific ML approaches suitable for each stage of the ICQP cycle.
• Guidelines for applying ML to diverse biotic and abiotic stress traits.

Conclusions:

• ML tools are indispensable for leveraging large-scale plant imaging data in stress phenotyping.
• The provided taxonomy and guidelines will enhance the adoption and effectiveness of ML in plant science.
• This work supports improved plant breeding strategies through advanced data analysis.