Water Stress Estimation from Leaf Turgor Pressure in `Arbequina' Olive Orchards Based on Linear Discriminant Analysis

26 Pages Posted: 7 Feb 2024

See all articles by Jaime Palomo

Jaime Palomo

University of Seville

Rafael Romero

affiliation not provided to SSRN

Maria Victoria Cuevas

affiliation not provided to SSRN

Teodoro Alamo

University of Seville

David Muñoz de la Peña

University of Seville

Abstract

In this work, we focus on estimating water stress based on leaf turgor pressure in `Arbequina' olive orchards. Currently, this analysis is time-consuming and primarily conducted by experts, rendering it impractical for integration into commercial deficit irrigation applications. Our study leverages a dataset that includes leaf turgor pressure measurements and meteorological variables collected between 2014 and 2019 from an orchard in Spain. We propose an innovative automated system based on machine learning techniques to classify trees into three distinct water stress levels by analyzing their daily trajectory. In this approach, both the probe and meteorological data undergo normalization before classification, employing principal component analysis and linear discriminant analysis.

Keywords: IoT, machine learning, expert system, Turgor pressure, Stem water potential, Irrigation scheduling

Suggested Citation

Palomo, Jaime and Romero, Rafael and Cuevas, Maria Victoria and Alamo, Teodoro and Muñoz de la Peña, David, Water Stress Estimation from Leaf Turgor Pressure in `Arbequina' Olive Orchards Based on Linear Discriminant Analysis. Available at SSRN: https://ssrn.com/abstract=4719404 or http://dx.doi.org/10.2139/ssrn.4719404

Jaime Palomo

University of Seville ( email )

Avda. del Cid s/n
Sevilla, 41004
Spain

Rafael Romero

affiliation not provided to SSRN ( email )

No Address Available

Maria Victoria Cuevas

affiliation not provided to SSRN ( email )

No Address Available

Teodoro Alamo

University of Seville ( email )

Avda. del Cid s/n
Sevilla, 41004
Spain

David Muñoz de la Peña (Contact Author)

University of Seville ( email )

Avda. del Cid s/n
Sevilla, 41004
Spain

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