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פותח על ידי קלירמאש פתרונות בע"מ -
Estimating evapotranspiration in screenhouses using artificial neural network models
Year:
2020
Source of publication :
Acta Horticulturae
Authors :
טייטל, מאיר
;
.
טנאי, יוסף
;
.
כהן, שלמה (הגנת הצומח)
;
.
לוקיאנוב, ויקטור
;
.
Volume :
1296
Co-Authors:

Tanny, J. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel; HIT - Holon Institute of Technology, POB 305, Holon, 58102, Israel

Neiman, M. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Lukyanov, V. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Cohen, S. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Teitel, M. - Institute of Agricultural Engineering, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Seginer, I. - Faculty of Civil Engineering, Technion, Israel Institute of Technology, Haifa, 32000, Israel

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

Increasing crop production under screens and in screenhouses has increased the interest in estimating crop evapotranspiration (ET) in this semi-protected environment. This study examines using artificial neural networks (ANN), for ET estimation, based on measured meteorological variables. Trained ANNs can provide ET estimates based on simple meteorological data. The experiment was carried out in a large banana screenhouse in the Carmel coast region, Israel, during a period of 52 days, from June to August 2016. Direct measurement of evapotranspiration was estimated with an eddy covariance system. Five internal micro-meteorological variables, air temperature and humidity, net and global radiation and air velocity, were measured on the same tower. The ANN model was implemented using MATLAB. All data collected during 52 days were divided into two periods: learning and verification. The ANN model was operated in two modes. The first included as input all 5 micro-meteorological variables and in the second, input data included different subsets of these variables. Running the model with all 5 variables gave a fit with a deviation of 7% from direct ET measurement and R2=0.94. In the second mode of operation, results showed that using only air temperature and global radiation as input data provided a reasonable estimation with a deviation of 8% from the measurement (R2=0.95). In summary, the results show that trained ANN models can provide accurate estimates of banana ET in a screenhouse based on micro-meteorological measurements.

Note:
Related Files :
Air velocity
energy balance
Radiation
relative humidity
temperature
עוד תגיות
תוכן קשור
More details
DOI :
10.17660/ActaHortic.2020.1296.101
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
52302
Last updated date:
02/03/2022 17:27
Creation date:
13/12/2020 19:02
Scientific Publication
Estimating evapotranspiration in screenhouses using artificial neural network models
1296

Tanny, J. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel; HIT - Holon Institute of Technology, POB 305, Holon, 58102, Israel

Neiman, M. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Lukyanov, V. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Cohen, S. - Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Teitel, M. - Institute of Agricultural Engineering, Agricultural Research Organization, HaMaccabim Road, POB 15159, Rishon LeZion, 7528809, Israel

 
Seginer, I. - Faculty of Civil Engineering, Technion, Israel Institute of Technology, Haifa, 32000, Israel

Estimating evapotranspiration in screenhouses using artificial neural network models

Increasing crop production under screens and in screenhouses has increased the interest in estimating crop evapotranspiration (ET) in this semi-protected environment. This study examines using artificial neural networks (ANN), for ET estimation, based on measured meteorological variables. Trained ANNs can provide ET estimates based on simple meteorological data. The experiment was carried out in a large banana screenhouse in the Carmel coast region, Israel, during a period of 52 days, from June to August 2016. Direct measurement of evapotranspiration was estimated with an eddy covariance system. Five internal micro-meteorological variables, air temperature and humidity, net and global radiation and air velocity, were measured on the same tower. The ANN model was implemented using MATLAB. All data collected during 52 days were divided into two periods: learning and verification. The ANN model was operated in two modes. The first included as input all 5 micro-meteorological variables and in the second, input data included different subsets of these variables. Running the model with all 5 variables gave a fit with a deviation of 7% from direct ET measurement and R2=0.94. In the second mode of operation, results showed that using only air temperature and global radiation as input data provided a reasonable estimation with a deviation of 8% from the measurement (R2=0.95). In summary, the results show that trained ANN models can provide accurate estimates of banana ET in a screenhouse based on micro-meteorological measurements.

Scientific Publication
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