Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives

Area of Science:

• Plant Science
• Computer Science
• Data Science

Background:

• Deep learning (DL) is a machine learning subset adept at handling large, diverse datasets for complex predictions.
• The plant science community increasingly uses DL to interpret high-throughput phenotyping and genotyping data.
• Digital image-based plant stress phenotyping is a growing area for DL applications.

Purpose of the Study:

• To review the application of deep learning principles in digital image-based plant stress phenotyping.
• To comparatively assess DL tools against traditional methods based on accuracy, data needs, and versatility.
• To identify future research directions for DL in plant science.

Main Methods:

• Literature review of recent studies employing DL for plant stress phenotyping.
• Comparative analysis of DL techniques versus existing methods.
• Evaluation of DL tools based on decision accuracy, data size requirements, and scenario applicability.

Main Results:

• Deep learning models show promise in accurately identifying plant stress from digital images.
• DL approaches offer advantages in handling large datasets compared to some traditional methods.
• The applicability of DL varies depending on the specific stress, plant species, and data available.

Conclusions:

• Deep learning is a powerful tool for advancing plant stress phenotyping and analysis.
• Further research is needed to optimize DL models and explore their full potential in diverse plant science applications.
• Comparative assessments are crucial for selecting appropriate DL tools for specific phenotyping challenges.