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.

翻译：作物持续面临不同环境条件的挑战。利用纳米材料进行种子处理是一种经济且环保的缓解作物环境胁迫的方案。本研究采用56种纳米种子引发处理来缓解玉米的环境胁迫。其中，7种选定的纳米引发处理在盐胁迫和高温干旱复合胁迫下，分别使胁迫抗性指数（SRI）显著提高了13.9%和12.6%。代谢组学数据显示，SRI值最高的ZnO纳米引发处理主要调控氨基酸代谢、次生代谢物合成、碳水化合物代谢及翻译等通路。由于纳米材料多样性及其与植物相互作用的复杂性，理解纳米种子引发机制仍存在困难。基于纳米引发数据，我们提出了一种基于可解释机器学习的可解释构效关系（ISAR）方法，用于预测和理解其胁迫缓解效应。结合机器学习的后验解释与基于模型的解释方法，可提供互补优势，为研究人员或政策制定者提供更具启发性和可信度的结果。研究发现，纳米颗粒的浓度、尺寸和Zeta电位是与盐胁迫下根干重相关的关键因素，并阐明了这些因素的影响及交互作用。此外，我们还开发了一个基于网页的交互工具，用于提供预测层面的解释，并收集特定纳米引发处理的更多细节。本研究为加速纳米材料在农业中的应用提供了一个有前景的框架，并可能对纳米安全性评估产生深远贡献。