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Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation
Hengjie Yu1,2, Dan Luo3, Sam Fong Yau Li4
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. fcheng@zju.edu.cn.
Nanoscale
|August 7, 2023
Summary
Seed nanopriming enhances crop stress resistance. This study introduces an interpretable machine learning approach to predict and understand nanomaterial effects on maize, aiding agricultural applications and nanosafety.
Area of Science:
- Agricultural Science
- Nanotechnology
- Plant Physiology
Background:
- Crops face environmental stresses impacting yield.
- Nanomaterial seed treatment offers a sustainable solution for stress mitigation.
- Understanding nanomaterial-plant interactions is complex.
Purpose of the Study:
- To evaluate seed nanopriming treatments for maize stress tolerance.
- To develop an interpretable machine learning model for predicting nanopriming efficacy.
- To identify key nanoparticle properties influencing stress resistance.
Main Methods:
- Screening of 56 seed nanopriming treatments in maize.
- Application of interpretable machine learning (ISAR) for predicting stress resistance.
- Metabolomic analysis of key nanopriming treatments (e.g., ZnO).
- Identification of dominant nanoparticle factors (concentration, size, zeta potential).
Main Results:
- Seven treatments significantly improved stress resistance index (SRI) under salinity and heat-drought.
- ZnO nanopriming showed the highest SRI, impacting amino acid and carbohydrate metabolism.
- ISAR model identified key nanoparticle properties influencing root dry weight under salinity stress.
Conclusions:
- Seed nanopriming is effective in enhancing maize stress tolerance.
- Interpretable machine learning provides a framework for understanding and predicting nanopriming effects.
- This approach accelerates nanomaterial application in agriculture and supports nanosafety assessments.
