Neural network soil moisture model for irrigation scheduling-Reference-Cited by-同舟云学术

Neural network soil moisture model for irrigation scheduling

Published:2021-01 Issue: Volume:180 Page:105801
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Gu Zhe,Zhu Tingting,Jiao Xiyun,Xu Junzeng,Qi Zhiming

Funder

National Key R&D Program of China

Fundamental Research Funds for the Central Universities

Project funded by China Postdoctoral Science Foundation

The Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

Hohai University

Publisher

Elsevier BV

Subject

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

Reference35 articles.

1. Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling;Adeyemi;Sensors (Basel),2018

2. The Root Zone Water Quality Model;Ahuja,2000

3. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration—Guidelines for computing crop water requirements. Food and Agriculture Organization of the United Nations (FAO) Irrigation and Drainage Paper 56. FAO, Rome.

4. The ASCE Standardized Reference Evapotranspiration Equation;Allen,2005

5. Water requirements;Allen,2007

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