Evaluating the Performance of Self-Organizing Maps to Estimate Well-Watered Canopy Temperature for Calculating Crop Water Stress Index in Indian Mustard (Brassica juncea)
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Volume 147, Issue 2
Abstract
The crop water stress index (CWSI) is a reliable indicator of water status in plants and has been utilized for stress monitoring, yield prediction, and irrigation scheduling. Despite this, however, its use is limited because its estimation requires baseline temperatures under similar environmental conditions, which can be problematic. In this study, field crop experiments were performed to monitor the canopy temperature of Indian mustard (Brassica juncea) from crop development through harvest under different irrigation treatment levels during the 2017 and 2018 growing seasons. Kohonen self-organizing map (KSOM), feed-forward neural network (FFNN), and multiple linear regression (MLR) models were developed for estimating the well-watered canopy temperature () using air temperature and relative humidity as input predictor variables. Comparisons were performed between model-estimated and measured values. The findings indicate that the KSOM-modeled values presented a better agreement with the measured values in comparison to MLR- and FFNN-based estimates, with values of 0.978, 0.924, and 0.923 for KSOM, MLR, and FFNN, respectively, during model validation. The dry canopy temperature was estimated to be air temperature plus 2°C. The CWSI computed using KSOM-based estimates of was compared with the CWSI obtained from measured values of . The results suggest a significant potential of KSOM for reliable estimation of the for calculating the CWSI that can be automated for developing precision irrigation systems.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (field experimental data). The SOM Toolbox (version 2.1) for MATLAB used in the present study is freely available to download from GITHUB (https://github.com/ilarinieminen/SOM-Toolbox).
Acknowledgments
The work received external funding from the UK Natural Environment Research Council (Award No. NE/N016394/1) and the Indian Ministry of Earth Science (Award No. MoES/NERC/IA-SWR/P3/10/2016-PC-II) through a scientific research collaborative project “Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat).” The study is an outcome of a visiting fellowship (ODF/2018/000374) awarded by Science and Engineering Research Board (SERB), Department of Science and Technology (DST) (Government of India) to Navsal Kumar for researching at Heriot-Watt University, Edinburgh (UK). The authors are grateful to the Institute for Infrastructure and Environment, Heriot-Watt University (UK), and the Department of Civil Engineering, NIT Hamirpur (India), for providing necessary technical guidance, experimental facilities, and support for the study. The authors are thankful to the three anonymous reviewers whose critical suggestions and feedback assisted the authors in improving the quality of the manuscript.
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Journal of Irrigation and Drainage Engineering
Volume 147 • Issue 2 • February 2021
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Received: Apr 19, 2020
Accepted: Aug 25, 2020
Published online: Nov 18, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 18, 2021
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