Estimation of Greenhouse Tomato Transpiration through Mathematical and Deep Neural Network Models Learned from Lysimeter Data-Reference-Cited by-同舟云学术

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Estimation of Greenhouse Tomato Transpiration through Mathematical and Deep Neural Network Models Learned from Lysimeter Data

Published:2023-10-31 Issue:4 Volume:32 Page:384-395
ISSN:1229-4675
Container-title:Journal of Bio-Environment Control
language:en
Short-container-title:J. Bio-Env. Con.

Author:

Andes Meanne P.¹,Roh Mi-young²,Lim Mi Young³,Choi Gyeong-Lee³,Jung Jung Su⁴,Kim Dongpil^ORCID

Affiliation:

1. Engineer II, Bureau of Plant Industry-Los Baños National Crop Research Development and Production Support Center 4030, Philippines

2. Senior Researcher, Protected Horticulture Research Institute, NIHHS, RDA, Haman 52054, Korea

3. Researcher, Protected Horticulture Research Institute, NIHHS, RDA, Haman 52054, Korea

4. Rural Extension Instructor, Gyeongsangbuk-do Provincial Agricultural Research and Extension Services, Daegu 41404, Korea

Publisher

The Korean Society for Bio-Environment Control

Subject

Psychiatry and Mental health

Link

https://www.ksbec.org/articles/pdf/doi/10.12791/KSBEC.2023.32.4.384.pdf

Reference43 articles.

1. A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method

2. Comparative performance of linear regression, polynomial regression and generalized additive model for canopy cover estimation in the dry deciduous forest of West Bengal

3. Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods

4. Long-term forecasting of monthly mean reference evapotranspiration using deep neural network: A comparison of training strategies and approaches

5. “Transpiration and crop yields” by C.T. de Wit, 1958, Institute of biological and chemical research of field crops and herbage, No. 64.6, Wageningen, the Netherlands

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