High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales

Area of Science:

• Computational biology and agricultural science.
• Integration of big data and high-performance computing in plant science.

Background:

• Advancements in data acquisition and analysis across biological scales are transforming agriculture.
• Computational methodologies like image segmentation and machine learning address complex agricultural and biological challenges.

Purpose of the Study:

• To review state-of-the-art methodologies in plant image segmentation and machine learning.
• To explore applications at agricultural, organ, and cellular scales.
• To discuss hardware, quantitative metrics, and biological mechanism extraction.

Main Methods:

• Review of image segmentation and machine learning techniques for plant image analysis.
• Comparison of methodologies across different biological scales (agricultural, organ, cellular).
• Discussion of hardware technologies and quantitative metrics.

Main Results:

• Image segmentation and machine learning enable modeling of genotype-environment interactions and root architecture.
• These methods facilitate accelerated gene identification and automated high-throughput phenotyping for disease detection.
• Methodologies vary based on physical characteristics at different scales.

Conclusions:

• Continued high-throughput imaging across scales offers significant opportunities for rapid scientific advancement.
• Understanding plant responses to stress and genotypic modifications can be enhanced through these computational approaches.
• The review highlights the diverse applications and evolving methodologies in plant image analysis.