Development of non-water-stressed baseline for realtime crop water stress monitoring

Name: Panupong Kumpipot

keyword: Crop water stress index (CWSI)

; Long short term memory (LSTM)

; Monitoring

; Data filtering

Abstract: In this work, we are interested in determining and using crop water status and quantify crop’s stress, an indicator called crop water stress index (CWSI) which is empirical approach and was developed by Idso et al. Our experiment has been conducted with the purpose of estimating CWSI of a durian tree around durian orchard under project called “IRRIGATION 4.0”, which is located at Chanthaburi, Thailand. Because real-time recorded dataset was scattered due to ever-changing environmental conditions. We find the correlation between stem water potential and CWSI of the durian tree from mid-November 2020 to mid-February 2021 to evaluate accuracy of calculated CWSI. From our experiment, the correlation coefficient was found to be -0.085 on average. Therefore, we propose a data cleansing framework that includes time, irradiance, wind speed, air relative humidity as parameters, and implemented long short-term memory (LSTM) to filter the dataset for estimating non-water-stressed baseline. The results show that this framework can produce correlation coefficient between stem water potential and CWSI to be equal to -0.671 on average, a much higher correlation compared to -0.085 when not using our framework

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Somsak Kittipiyakul

Role: advisor

Created: 2021

Modified: 2023-04-19

Issued: 2023-04-19

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2021.566

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Artificial Intelligence and Internet of Things

Grantor: Thammasat University

©copyrights Thammasat University

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