Home // CADAT 2025, The Second International Conference on Accessible Digital Agriculture Technologies // View article
Authors:
Laura Teresa Martínez Marquina
José Ramón Torres Martín
José Manuel Velarde Gestera
Miguel Román Écija
Guillermo León Ropero
José Luis Trapero Casas
Juan Antonio Navas Cortés
Mihaela Ioana Chidean
Inmaculada Mora Jiménez
Keywords: bootstrap-based non-parametric test; leaf scorch; physiological indices; hyperspectral indices; precision agriculture.
Abstract:
Xylella fastidiosa (Xf) is one of the most aggressive vascular pathogens threatening woody crops, particularly almond trees, in the Mediterranean region. This paper presents a statistical framework for the early detection of Xf infection prior to the onset of visible symptoms, leveraging multitemporal physiological and spectral data collected at the leaf level. The approach integrates measurements from porometry, fluorometry, and spectrometry with a non-parametric bootstrap resampling method to identify traits that differentiate health states and reveal physiological responses linked to disease progression. Results reveal that Xf-infected trees, which later develop visible symptoms, exhibit significant differences in median values of both spectral indices and physiological variables compared to healthy and intermediate health groups. Grounded in real field data, this work contributes to data-driven plant health monitoring and precision agriculture, demonstrating the potential of combining physiological and spectral indicators for early, non-invasive diagnosis of vascular diseases in perennial crops. The findings support the development of predictive tools for timely disease detection and management in almond and olive orchards.
Pages: 1 to 8
Copyright: Copyright (c) IARIA, 2025
Publication date: October 26, 2025
Published in: conference
ISBN: 978-1-68558-328-6
Location: Barcelona, Spain
Dates: from October 26, 2025 to October 30, 2025