Next Article in Journal
Effective Biocontrol of Rice Blast through Dipping Transplants and Foliar Applications
Previous Article in Journal
Driving Style Assessment System for Agricultural Tractors: Design and Experimental Validation
Previous Article in Special Issue
Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 12
Issue 3
10.3390/agronomy12030591
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System

by
Xuebo Jin
1,2,
Jiashuai Zhang
1,
Jianlei Kong
1,2,*,
Tingli Su
1 and
Yuting Bai
1
1
Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
2
National Engineering Laboratory for Agri-Product Quality Traceability, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(3), 591; https://doi.org/10.3390/agronomy12030591
Submission received: 22 January 2022 / Revised: 17 February 2022 / Accepted: 25 February 2022 / Published: 27 February 2022
(This article belongs to the Special Issue Application of Artificial Neural Networks in Agriculture)
Browse Figures
Versions Notes

Abstract

Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System.
Keywords: normalization; time series prediction; reversible normalization; deep learning; automatic normalization; Smart Agriculture System normalization; time series prediction; reversible normalization; deep learning; automatic normalization; Smart Agriculture System

Share and Cite

MDPI and ACS Style

Jin, X.; Zhang, J.; Kong, J.; Su, T.; Bai, Y. A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System. Agronomy 2022, 12, 591. https://doi.org/10.3390/agronomy12030591

AMA Style

Jin X, Zhang J, Kong J, Su T, Bai Y. A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System. Agronomy. 2022; 12(3):591. https://doi.org/10.3390/agronomy12030591

Chicago/Turabian Style

Jin, Xuebo, Jiashuai Zhang, Jianlei Kong, Tingli Su, and Yuting Bai. 2022. "A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System" Agronomy 12, no. 3: 591. https://doi.org/10.3390/agronomy12030591

APA Style

Jin, X., Zhang, J., Kong, J., Su, T., & Bai, Y. (2022). A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System. Agronomy, 12(3), 591. https://doi.org/10.3390/agronomy12030591

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop