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A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance

  • Published:2021-11 Issue: Volume:190 Page:106435
  • ISSN:0168-1699
  • Container-title:Computers and Electronics in Agriculture
  • language:en
  • Short-container-title:Computers and Electronics in Agriculture

Author:

Yao Jie-Peng,Wang Zi-Yang,de Oliveira Ricardo Ferraz,Wang Zhong-Yi,Huang Lan

Funder

National Natural Science Foundation of China

National Institutes of Health

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference40 articles.

1. Wheat yield potential in controlled-environment vertical farms;Asseng;Proc. Natl. Acad. Sci.,2020

2. Risk-averse optimization of crop inputs using a deep ensemble of convolutional neural networks;Barbosa;Comput. Electron. Agric.,2020

3. Re-modeling Chara action potential: I. From Thiel model of Ca2+transient to action potential form;Beilby;AIMS Biophys.,2016

4. The use of the area under the ROC curve in the evaluation of machine learning algorithms;Bradley;Pattern Recogn.,1997

5. Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response;Chatterjee;Sens. Actuators B-Chem.,2017

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