bioRxiv · 10.64898/2026.09.03.745400
Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning
Abstract
Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.
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Ferez-Gomez, A., Soler-Escamez, L., Ferrer-Blanco, C., Perez-Aguilar, A., Hormaza, J. I., Diaz-Zayas, A., Losada, J. M.. 2026-09-07. Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning. https://doi.org/10.64898/2026.09.03.745400
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