Understanding plant phenotypes in crop breeding through explainable AI.

Area of Science:

• Plant Science
• Artificial Intelligence
• Computational Biology

Background:

• Machine learning (ML) is increasingly used in plant phenotyping for rapid trait measurement and phenotype prediction using image data.
• Lack of interpretability in ML models hinders understanding of biological processes driving plant phenotypes.
• Explainable AI (XAI) offers methods to understand ML predictions, identify influential features, and improve model reliability.

Purpose of the Study:

• To introduce Explainable AI (XAI) concepts and algorithms for plant phenotyping.
• To demonstrate how XAI can be used to gain biological insights from ML models.
• To highlight the importance of transparency and interpretability in ML for plant science.

Main Methods:

• Review of current XAI algorithms and their suitability for different data types and ML models.
• Analysis of studies applying XAI to plant phenotyping data.
• Framework development for integrating XAI into plant phenotyping workflows.

Main Results:

• XAI techniques enable researchers to identify critical features influencing plant phenotype predictions.
• Model explanations help in sanity-checking ML models and detecting dataset biases.
• Recent studies successfully used XAI to link specific features to plant trait variations.

Conclusions:

• XAI is crucial for unlocking the full potential of ML in plant phenotyping by providing interpretability.
• Integrating XAI into plant science research enhances understanding of complex biological mechanisms.
• Transparency and interpretability are essential for reliable and applicable ML models in diverse environmental conditions.