Original Articles Open access

Handheld sensing system for data-driven irrigation management in olive orchards

Authors

Published
5 September 2025
Section
Original Articles
DOI
10.4081/jae.2025.1960

Abstract

Proper water management is necessary to optimize the quantity and quality of olive oil. The advent of monitoring tools capable of providing reliable and systematic information on tree water status, and thus vegetative growth, can be advantageous for growers of olive groves. Leaf temperature, environmental conditions, Crop Water Stress Index (CWSI), and other related vegetation indices (VI) measured non-invasively using proximal sensing tools are being increasingly employed for agriculture 4.0 applications. This study aimed to implement and evaluate a low-cost handheld system to determine the water conditions of olive trees under different irrigation treatments. Implementation of the data-driven system required the selection of the most efficient CWSI equation for the developed proximal sensing device. Specifically, five potential equations were evaluated, including two analytical models, one empirical equation derived from existing literature, a newly proposed empirical equation, and a hybrid model combining analytical and empirical calculations. The sensing system was equipped with a Global Navigation Satellite System (GNSS), an infrared thermometer, a compact NDVI sensor with ambient light correction, and an environmental measuring unit providing air temperature and relative humidity. Additionally, the leaf water potential (LWP) was calculated in real time to better determine the actual hydric stress conditions of the trees. All data were acquired between 12:00 and 14:00 on both the sunny and shaded canopy sides. The experimental results showed that the handheld system eased the collection of field data to help growers schedule and manage irrigation for olive oil production through stress identification and precise GPS positioning. The best correlation between LWP and CWSI was found for the analytical formulas (R2 = 0.62), followed by the empirical formula (R2 = 0.55); however, both analytical equations required a higher number of measurements when compared to the alternative models considered, which complicated their practical implementation in the handheld prototype.

Keywords

Supporting Agencies

This work was supported by the project “Piattaforma DIGItale per La difesa di precisione dell’OliVEto” –“DIGILOVE”,- “National Research Centre for Agricultural Technologies (Agritech)” (MUR, PNRR-M4C2, CN00000022), Spoke 2 ’Università degli Studi di Napoli Federico II’, CUP: E63C22000920005

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