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Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal

Grace D Tan1,2, Ushasi Chaudhuri3, Sebastian Varela4,5

  • 1Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

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Summary
This summary is machine-generated.

Artificial intelligence and machine learning (AI/ML) models can rapidly estimate stomatal traits from plant images. However, their application is often limited by phenotypic diversity, necessitating further research for broader utility.

Keywords:
Artificial intelligencecomputer visionmachine learningobject detectionplant biologyplant phenotypingsegmentationstomatastomatal density

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Area of Science:

  • Plant Biology
  • Computational Biology
  • Image Analysis

Background:

  • Artificial intelligence and machine learning (AI/ML) offer powerful tools for analyzing large biological image datasets.
  • Estimating plant trait data from images is a key application, with stomatal traits being of significant interest.

Purpose of the Study:

  • To review 39 papers on AI/ML models for estimating stomatal traits from epidermal micrographs.
  • To provide plant biologists with an understanding of AI/ML capabilities and limitations in this field.
  • To summarize current advancements and challenges in AI/ML-enabled stomatal phenotyping.

Main Methods:

  • Systematic literature review of 39 research papers.
  • Analysis of AI/ML model development and application for stomatal trait estimation.
  • Evaluation of model performance across genetic, environmental, and developmental variations.

Main Results:

  • Most AI/ML models achieve human-level performance for stomatal density quantification at high speeds.
  • Model generalizability is often limited across diverse phenotypic variations.
  • Some models predict additional traits but require substantial ground-truth data generation.

Conclusions:

  • AI/ML shows great promise for accelerated stomatal phenotyping.
  • Challenges remain in model applicability across phenotypic diversity.
  • Future work should focus on advancing AI/ML-enabled computer vision for broader plant trait analysis.