数字农科院2.0

Development potential of nanoenabled agriculture projected using machine learning

文献类型： 外文期刊

作者： Peng Deng;Yiming Gao;Li Mu;Xiangang Hu;Fubo Yu;Yuying Jia;Zhenyu Wang;Baoshan Xing

作者机构：

关键词： artificial intelligence;machine learning;nanoenabled agriculture;nanoparticle;risk

期刊名称： Proceedings of the National Academy of Sciences of the United States of America

ISSN： 1091-6490

年卷期： 2023 年 120 卷 25 期

页码：

收录情况： SCIE（2023版）

摘要： The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R2 higher than 0.8 for 13 random forest models) the response and uptake/transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales.

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