Original Articles
5 September 2025
Early Access
Handheld sensing system for data-driven irrigation management in olive orchards
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
0
Views
0
Downloads
Authors
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.
Altmetrics
Downloads
Download data is not yet available.
Citations
Agam, N., Cohen, Y., Berni, J., Alchanatis, V., Kool, D., Dag, A., et al. 2013. An insight to the performance of crop water stress index for olive trees. Agric. Water Manag. 118:79–86.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome. Available from: https://www.fao.org/4/x0490e/x0490e00.htm
Bastiaanssen, W.G., Menenti, M., Feddes, R., Holtslag, A. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212:198–212.
Bellvert, J., Marsal, J., Girona, J., Gonzalez-Dugo, V., Fereres, E., Ustin, S.L., Zarco-Tejada, P.J. 2016. Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and Saturn peach orchards. Remote Sens. 8:39.
Bellvert, J., Zarco-Tejada, P. J., Girona, J., Fereres, E. 2014. Mapping crop water stress index in a ‘Pinot-noir’vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precis. Agric. 15:361-376.
Ben-Gal, A., Agam, N., Alchanatis, V., Cohen, Y., Yermiyahu, U., Zipori, I., et al. 2009. Evaluating water stress in irrigated olives: correlation of soil water status, tree water status, and thermal imagery. Irrig. Sci. 27:367-376.
Berni, J., Zarco-Tejada, P., Sepulcre-Cantó, G., Fereres, E., Villalobos, F. 2009. Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens. Environ. 113:2380-2388.
Caruso, G., Gucci, R., Urbani, S., Esposto, S., Taticchi, A., Di Maio, I., et al. 2014. Effect of different irrigation volumes during fruit development on quality of virgin olive oil of cv. Frantoio. Agric. Water Manag. 134:94–103.
Caruso, G., Palai, G., D’onofrio, C., Gucci, R. 2019. Sustainable management of water and soil in olive orchards and vineyards under climate change. Agrochimica 2019:125-132.
Catania, P., Orlando, S., Roma, E., Vallone, M. 2021. Vineyard design supported by GPS application. Acta Hortic. 1314:227-234.
Catania, P., Comparetti, A., Febo, P., Morello, G., Orlando, S., Roma, E., Vallone, M. 2020. Positioning accuracy comparison of GNSS receivers used for mapping and guidance of agricultural machines. Agronomy 10:924.
Catania, P., Roma, E., Orlando, S., Vallone, M. 2023. Evaluation of multispectral data acquired from UAV platform in olive orchard. Horticulturae 9:133.
Chehbouni, A., Nouvellon, Y., Lhomme, J.-P., Watts, C., Boulet, G., Kerr, Y., et al. 2001. Estimation of surface sensible heat flux using dual angle observations of radiative surface temperature. Agric. For. Meteorol. 108:55–65.
Clawson, K.L., Blad, B.L. 1982. Infrared thermometry for scheduling irrigation of corn 1. Agron. J. 74:311–316.
Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y., Tsipris, J. 2005. Estimation of leaf water potential by thermal imagery and spatial analysis. J. Exp. Bot. 56:1843–1852.
Egea, G., Padilla-Díaz, C.M., Martinez-Guanter, J., Fernández, J.E., Pérez-Ruiz, M. 2017. Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agric. Water Manag. 187:210-221.
Facchi, A., Gharsallah, O., Gandolfi, C. 2013. Evapotranspiration models for a maize agro-ecosystem in irrigated and rainfed conditions. J. Agric. Eng. 44:411.
Fernández, J.E., Diaz-Espejo, A., Romero, R., Hernandez-Santana, V., García, J.M., Padilla-Díaz, C.M., Cuevas, M.V. 2018. Precision irrigation in olive (Olea europaea L.) tree orchards. In: I.F. Garcia Tejero, V.H. Duran Zuazo (eds.), Water scarcity and sustainable agriculture in semiarid environment. Cambridge, Academic Press. pp. 179–217.
Gardner, B., Nielsen, D., Shock, C. 1992. Infrared thermometry and the crop water stress index. I. History, theory, and baselines. J. Prod. Agr. 5:462–466.
Gonzalez-Dugo, V., Zarco-Tejada, P., Berni, J.A., Suarez, L., Goldhamer, D., Fereres, E. 2012. Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent. Agric. For. Meteorol. 154:156-165.
Guilioni, L., Jones, H., Leinonen, I., Lhomme, J.-P. 2008. On the relationships between stomatal resistance and leaf temperatures in thermography. Agric. For. Meteorol. 148:1908–1912.
Hillel, D. 2013. Fundamentals of soil physics. Cambridge, Academic Press.
Huband, N., Monteith, J. 1986. Radiative surface temperature and energy balance of a wheat canopy. Bound. Layer Meteorol. 36:1-17.
Idso, S.B. 1982. Non-water-stressed baselines: a key to measuring and interpreting plant water stress. Agric. Meteorol. 27:59–70.
Idso, S., Jackson, R., Pinter Jr, P., Reginato, R., Hatfield, J. 1981. Normalizing the stress-degree-day parameter for environmental variability. Agric. Meteorol. 24:45–55.
Irmak, S., Haman, D.Z., Bastug, R. 2000. Determination of crop water stress index for irrigation timing and yield estimation of corn. Agron. J. 92:1221–1227.
Jackson, J., Saborio, R., Ghazanfar, S.A., Gebre-Egziabher, D., Davis, B. 2018. Evaluation of low-cost, centimeter-level accuracy OEM GNSS receivers. Report Number: MN/RC 2018-10. Available from: https://rosap.ntl.bts.gov/view/dot/35402
Jackson, R.D., Idso, S., Reginato, R., Pinter Jr, P. 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17:1133–1138.
Jones, H.G. 1992. Plants and microclimate: a quantitative approach to environmental plant physiology. Cambridge, Cambridge University Press.
Jones, H.G. 1999. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric. For. Meteorol. 95:139–149.
Jones, H.G. 2013. Plants and microclimate: a quantitative approach to environmental plant physiology. Cambridge, Cambridge University Press.
Jones, H.G., Archer, N., Rotenberg, E., Casa, R. 2003. Radiation measurement for plant ecophysiology. J. Exp. Bot. 54:879–889.
Jones, H.G., Stoll, M., Santos, T., de Sousa, C., Chaves, M.M., Grant, O.M. 2002. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J. Exp. Bot. 53:2249–2260.
Karaim, M., Elsheikh, M., Noureldin, A., Rustamov, R. 2018. GNSS error sources. In: R.B. Rustamov, A.M. Hashimov (eds.), Multifunctional operation and application of GPS. IntechOpen. pp. 69–85.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F. 2006. World map of the Köppen-Geiger climate classification updated. Meteorol. Z. 15:259-263.
Li, L., Nielsen, D., Yu, Q., Ma, L., Ahuja, L. 2010. Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agric. Water Manag. 97:1146–1155.
Liang, S. 2004. Quantitative remote sensing of land surfaces. Hoboken, Wiley.
Maes, W., Steppe, K. 2012. Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. J. Exp. Bot. 63:4671-4712.
Maes, W.H., Achten, W., Reubens, B., Muys, B. 2011. Monitoring stomatal conductance of Jatropha curcas seedlings under different levels of water shortage with infrared thermography. Agric. For. Meteorol. 151:554-564.
Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., et al. 2007. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J. Exp. Bot. 58:827–838.
Monteith, J. 1973. Principles of environmental physics. London, Edward Arnold.
Moriana, A., Fereres, E. 2002. Plant indicators for scheduling irrigation of young olive trees. Irrig. Sci. 21:83–90.
Moriana, A., Pérez-López, D., Prieto, M., Ramírez-Santa-Pau, M., Pérez-Rodriguez, J. 2012. Midday stem water potential as a useful tool for estimating irrigation requirements in olive trees. Agric. Water Manag. 112:43–54.
Moriana, A., Villalobos, F., Fereres, E. 2002. Stomatal and photosynthetic responses of olive (Olea europaea L.) leaves to water deficits. Plant Cell Environ. 25:395–405.
Oerke, E-C, Fröhling, P., Steiner, U. 2011. Thermographic assessment of scab disease on apple leaves. Precis. Agric. 12:699–715.
Oerke, E-C., Steiner, U., Dehne, H.-W., Lindenthal, M. 2006. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J. Exp. Bot. 57:2121–2132.
O’Toole, J., Real, J. 1986. Estimation of aerodynamic and crop resistances from canopy temperature 1. Agron. J. 78:305–310.
Raupach, M. 1992. Drag and drag partition on rough surfaces. Bound. Layer Meteorol. 60:375–395.
Roma, E., Catania, P. 2022. Precision oliviculture: research topics, challenges, and opportunities - A review. Remote Sens. 14:1668.
Roma, E., Laudicina, V.A., Vallone, M., Catania, P. 2023. Application of precision agriculture for the sustainable management of fertilization in olive groves. Agronomy 13:324.
Sepulcre-Cantó, G., Zarco-Tejada, P.J., Jiménez-Muñoz, J., Sobrino, J., De Miguel, E., Villalobos, F.J. 2006. Detection of water stress in an olive orchard with thermal remote sensing imagery. Agric. For. Meteorol. 136:31–44.
Sghaier, A., Dhaou, H., Jarray, L., Abaab, Z., Sekrafi, A., Ouessar, M. 2022. Assessment of drought stress in arid olive groves using HidroMORE model. J. Agric. Eng. 53:1264.
Testi, L., Goldhamer, D., Iniesta, F., Salinas, M. 2008. Crop water stress index is a sensitive water stress indicator in pistachio trees. Irrig. Sci. 26:395–405.
Testi, L., Orgaz, F., Villalobos, F.J. 2006. Variations in bulk canopy conductance of an irrigated olive (Olea europaea L.) orchard. Environ. Exp. Bot. 55:15–28.
Thom, A., Oliver, H.R. 1977. On Penman’s equation for estimating regional evaporation. Q. J. R. Meteorol. Soc. 103:345–357.
Tognetti, R., d’Andria, R., Lavini, A., Morelli, G. 2006. The effect of deficit irrigation on crop yield and vegetative development of Olea europaea L.(cvs. Frantoio and Leccino). Eur. J. Agron. 25:356–364.
Tous, J., Hermoso, J., Romero, A. 2010. New trends in olive orchard design for continuous mechanical harvesting. Adv. Hort. Sci. 24:43:52
Vanella, D., Ferlito, F., Torrisi, B., Giuffrida, A., Pappalardo, S., Saitta, D., et al. 2021. Long-term monitoring of deficit irrigation regimes on citrus orchards in Sicily. J. Agric. Eng. 52:1193.
Veysi, S., Naseri, A.A., Hamzeh, S., Bartholomeus, H. 2017. A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agric. Water Manag. 189:70–86.
Villalobos, F., Testi, L., Hidalgo, J., Pastor, M., Orgaz, F. 2006. Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur. J. Agron. 24:296–303.
Wang, X., Yang, W., Wheaton, A., Cooley, N., Moran, B. 2010. Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring. Comput. Electron. Agr. 73:74–83.
Yuan, G., Luo, Y., Sun, X., Tang, D. 2004. Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain. Agric. Water Manag. 64:29–40.
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: E63C22000920005How to Cite
“Handheld sensing system for data-driven irrigation management in olive orchards ” (2025) Journal of Agricultural Engineering [Preprint]. doi:10.4081/jae.2025.1960.
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
