Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation-Reference-Cited by-同舟云学术

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

Published:2023 Issue:32 Volume:15 Page:13437-13449
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Yu Hengjie¹²^ORCID,Luo Dan³,Li Sam Fong Yau⁴^ORCID,Qu Maozhen¹²,Liu Da¹²,He Yingchao¹²,Cheng Fang¹²^ORCID

Affiliation:

1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

2. Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China

3. Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York 14853, USA

4. Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore

Abstract

An interpretable structure–activity relationship (ISAR) approach, based on interpretable machine learning, was proposed to predict and understand the stress mitigation effects of seed nanopriming.

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/NR/D3NR02322B

Reference54 articles.

1. Advanced nanomaterials in agriculture under a changing climate: The way to the future?

2. Plant molecular stress responses face climate change

3. Nano-Biotechnology in Agriculture: Use of Nanomaterials to Promote Plant Growth and Stress Tolerance

4. Nano-enabled strategies to enhance crop nutrition and protection

5. Copper Nanoparticle Application Enhances Plant Growth and Grain Yield in Maize Under Drought Stress Conditions

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