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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Development potential of nanoenabled agriculture projected using machine learning.

Peng Deng¹, Yiming Gao¹, Li Mu²

¹Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.

Proceedings of the National Academy of Sciences of the United States of America

|June 14, 2023

Summary

Machine learning predicts nano-enabled agriculture potential by analyzing nanoparticle (NP) plant interactions. Factors like NP dose, plant age, and temperature influence outcomes, with Africa showing promise for sustainable farming.

Keywords:

artificial intelligence machine learning nanoenabled agriculture nanoparticle risk

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Area of Science:

Agricultural Science
Environmental Science
Nanotechnology

Background:

Nanoparticles (NPs) offer precise and sustainable solutions in agriculture.
The full development potential of nano-enabled agriculture is not yet understood.
Understanding NP-plant interactions is crucial for optimizing agricultural applications.

Purpose of the Study:

To predict the response and uptake/transport of various NPs by plants using machine learning.
To identify key factors influencing NP-plant interactions.
To project the future development potential of nano-enabled agriculture.

Main Methods:

Construction of a comprehensive NP-plant database with 1,174 datasets.
Application of machine learning, specifically 13 random forest models (R² > 0.8).
Multiway feature importance, interaction, and covariance analysis for model interpretability.

Main Results:

Plant responses to NPs are significantly influenced by exposure dose, duration, plant age, NP size, and zeta potential.
Fe2O3 NP application may inhibit bean growth in Europe due to low temperatures, while Africa's high temperatures mitigate oxidative stress.
Africa is predicted to be a suitable region for nano-enabled agriculture.

Conclusions:

Nano-enabled agriculture's potential is significant but complicated by regional differences and temperature variations.
Future temperature increases may reduce NP-induced oxidative stress in crops like beans and maize.
Further field studies are essential to validate predictions and address country/continental scale differences.