Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation

Kavli Affiliate: Dan Luo

| First 5 Authors: Hengjie Yu, Dan Luo, Sam F. Y. Li, Maozhen Qu, Da Liu

| Summary:

Crops are constantly challenged by different environmental conditions. Seed
treatment by nanomaterials is a cost-effective and environmentally-friendly
solution for environmental stress mitigation in crop plants. Here, 56 seed
nanopriming treatments are used to alleviate environmental stresses in maize.
Seven selected nanopriming treatments significantly increase the stress
resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined
heat-drought stress, respectively. Metabolomics data reveals that ZnO
nanopriming treatment, with the highest SRI value, mainly regulates the
pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate
metabolism, and translation. Understanding the mechanism of seed nanopriming is
still difficult due to the variety of nanomaterials and the complexity of
interactions between nanomaterials and plants. Using the nanopriming data, we
present an interpretable structure-activity relationship (ISAR) approach based
on interpretable machine learning for predicting and understanding its stress
mitigation effects. The post hoc and model-based interpretation approaches of
machine learning are combined to provide complementary benefits and give
researchers or policymakers more illuminating or trustworthy results. The
concentration, size, and zeta potential of nanoparticles are identified as
dominant factors for correlating root dry weight under salinity stress, and
their effects and interactions are explained. Additionally, a web-based
interactive tool is developed for offering prediction-level interpretation and
gathering more details about specific nanopriming treatments. This work offers
a promising framework for accelerating the agricultural applications of
nanomaterials and may profoundly contribute to nanosafety assessment.

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