Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Area of Science:

• Plant Science
• Agricultural Science
• Computational Biology

Background:

• Plant stress significantly impacts crop fitness and food production.
• Hormesis, a phenomenon where low-dose stressors stimulate positive traits, offers potential for crop improvement.
• Hormesis management, involving controlled stress exposure, aims to boost crop productivity and quality.

Purpose of the Study:

• To review recent advancements in Machine Learning (ML) and Deep Learning (DL) applications in plant stress science.
• To explore the potential of AI in overcoming the complexities of plant stress responses.
• To highlight the role of ML and DL in developing improved hormesis management protocols.

Main Methods:

• Review of current literature on ML and DL in plant stress research.
• Analysis of AI's role in interpreting multi-layered plant defense response data.
• Focus on applications in modeling genomic variation, gene/protein expression, and metabolite biosynthesis.

Main Results:

• Technological advancements generate extensive datasets on plant stress responses.
• AI tools, particularly ML and DL, are crucial for processing and interpreting these complex datasets.
• ML and DL enable accurate modeling of plant stress responses, aiding scientific understanding.

Conclusions:

• AI, especially ML and DL, is vital for understanding complex plant physiological responses to stress.
• These technologies are essential for developing effective hormesis management strategies.
• The integration of AI holds significant promise for enhancing crop productivity and quality through optimized stress management.