Asymetree was born at the CSIC Institute for Sustainable Agriculture. Every irrigation recommendation SAQIA gives rests on published, peer-reviewed research — and here you can check it.
Asymetree is a spin-off of the Institute for Sustainable Agriculture (IAS-CSIC) in Córdoba, Spain. Our origin is the institute’s Agronomy group, which has spent decades studying water–plant relations in Mediterranean tree crops: how much water an olive or almond tree really needs, at which moments of the season a deficit costs yield, and at which it is tolerated without penalty.
That knowledge — crop coefficients, stress thresholds, deficit irrigation strategies — lived in scientific papers and field trials. SAQIA is the way to bring it to the farm: the same methods, applied to your trees, with your data.
A tree that transpires well cools itself; one that is thirsty heats up. Our team pioneered measuring that phenomenon from drones with thermal cameras and turning it into an operational index, the CWSI, which detects water stress tree by tree before it becomes visible.
Canopy volume determines how much water each tree demands. With LiDAR, the structure of the whole orchard — height, canopy, missing trees, topography — is measured in one morning with centimetre precision. It is the baseline against which any improvement is documented.
Specific crop coefficients and regulated deficit irrigation: applying the deficit in the phenological windows where the tree tolerates it and irrigating in those where it doesn’t. Decades of IAS-CSIC trials in olive and almond underpin the judgement with which SAQIA calculates every irrigation plan.
A selection of the work by our team and the IAS-CSIC group that SAQIA builds on. All linked to the original publication.
Rafael Orozco-Moran · Jose A. Jimenez-Berni · Elias Fereres · Francisco Orgaz
Scientia Horticulturae, 2026, 357, 114652
Quantifies the impact of tree structural heterogeneity, estimated from LiDAR-derived ground cover, on water–yield relationships in a commercial almond orchard. Conventional irrigation management ignores tree-size variability within management units, leading to inefficient water use.
doi.org/10.1016/j.scienta.2026.114652Rafael Orozco-Moran · Jose A. Jimenez-Berni · Elias Fereres · Francisco Orgaz
Agricultural Water Management, 2025, 313, 109455
Almond irrigation scheduling methodology integrating the water balance with remote and proximal sensing for individualized crop coefficient (Kc) estimation. Characterizing each tree with LiDAR and multispectral sensors allows irrigation doses to match the orchard’s real heterogeneity.
doi.org/10.1016/J.AGWAT.2025.109455Jose A. Jimenez-Berni · D.M. Deery · P. Rozas-Larraondo · A.T.G. Condon · G.J. Rebetzke · R.A. James · W.D. Bovill · R.T. Furbank · X.R.R. Sirault
Frontiers in Plant Science, 2018, 9, 237
High-throughput phenotyping methodology using ground-based LiDAR to automatically determine plant height, crown cover and above-ground biomass under field conditions, directly applicable to structural crop characterization.
doi.org/10.3389/fpls.2018.00237M. López-López · M. Espadafor · L. Testi · I.J. Lorite · Francisco Orgaz · Elias Fereres
Irrigation Science, 2018, 36 (4–5)
Characterizes water requirements of mature almond trees under varying atmospheric demand in Mediterranean conditions, providing specific crop coefficients that underpin more efficient irrigation scheduling.
doi.org/10.1007/s00271-018-0582-2D. Moldero · Á. López-Bernal · L. Testi · I.J. Lorite · Elias Fereres · Francisco Orgaz
Irrigation Science, 2021, 39 (4)
Multi-season analysis of deficit irrigation effects on almond yield. Identifies water-reduction thresholds that optimize marginal productivity and the crop’s economic sustainability under scarcity.
doi.org/10.1007/s00271-021-00720-8J.A.J. Berni · P.J. Zarco-Tejada · L. Suárez · Elias Fereres
IEEE Transactions on Geoscience and Remote Sensing, 2009, 47 (3), 722–738
First operational thermal and multispectral drone-based remote sensing system for monitoring vegetation water and nutritional status at individual-tree scale. An international reference work in drone-based agricultural remote sensing.
doi.org/10.1109/TGRS.2008.2010457J.A.J. Berni · P.J. Zarco-Tejada · G. Sepulcre-Cantó · Elias Fereres · F. Villalobos
Remote Sensing of Environment, 2009, 113 (11), 2380–2388
Methodology for mapping canopy conductance and the Crop Water Stress Index (CWSI) in olive orchards using high-resolution thermal imagery. Establishes the feasibility of detecting water stress tree by tree — the basis of differentiated irrigation management in woody crops.
doi.org/10.1016/j.rse.2009.06.018Technical visits, irrigation plans and end-of-season reviews are signed by people with a name, a face and a verifiable scientific track record.
Co-founder · Precision agronomy
Specialist in precision management of woody crops. Has developed and validated LiDAR structural characterization methodologies and plant-sensor-based irrigation scheduling in commercial almond and olive orchards.
Co-founder · Sensors & remote sensing
Expert in drone remote sensing and proximity sensors for crop status diagnosis. Pioneer in the operational use of drone-mounted thermal cameras for agriculture.
Co-founder · Hardware & sensor engineering
Industrial engineer responsible for the design and manufacturing of the plant sensors and the field communications network. Integrates hardware, firmware and LoRaWAN systems for on-farm deployments.
«When SAQIA recommends an irrigation, there is no black box behind it: there is published scientific literature and years of fieldwork.»
Tell us your crop, your area and how you irrigate today. We reply within 48 working hours.
