My doctoral thesis is developed in the field of irrigation optimization in fruit trees, specifically investigating the detection of the variability of behavior of fruit trees in the face of hydric stress. The research is carried out at the AgroPhenoLab — Institute of Sustainable Agriculture (IAS-CSIC) in collaboration with the University of Córdoba (UCO).
The core challenge I address is that almond orchards — and fruit trees in general — do not respond uniformly to irrigation. There is significant intra-plot variability driven by differences in canopy size, soil heterogeneity, and root distribution. Standard irrigation scheduling based on average field values fails to account for this variability, leading to either water waste or stress-induced yield loss.
My approach combines aerial and terrestrial LiDAR for precise canopy characterization, UAV-mounted thermal cameras (calibrated using the PyRADTEMCam methodology) for water stress detection, and IoT sensor networks for continuous micro-meteorological monitoring. These data streams feed into models that can predict and prescribe irrigation at the individual-tree level.
The research builds directly on the STIMA2 project (Single Tree Irrigation Management Algorithms for Almonds), which demonstrated that matching irrigation to actual evapotranspiration — accounting for individual tree canopy area — can maximize yield and minimize water input simultaneously.
The thesis is expected to contribute novel frameworks for precision irrigation at the single-tree scale, with applications to olive, citrus, and other woody crops beyond almond.