Development potential of nanoenabled agriculture projected using machine learning.
Proc Natl Acad Sci U S A
; 120(25): e2301885120, 2023 06 20.
Article
em En
| MEDLINE
| ID: mdl-37314934
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Agricultura
/
Nanopartículas
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
País/Região como assunto:
Africa
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article