IoT technology as a support tool for the calculation of Crop Water Stress Index in a Vitis vinifera L. cv. Chardonnay vineyard in Northern Italy

Authors

DOI:

https://doi.org/10.36253/ijam-2747

Keywords:

drought stress, precision agriculture, leaf temperature, irrigation management, CWSI

Abstract

Nowadays agriculture is one of the main sectors affected by climate change. The continuous increase of temperature and drought periods are posing serious problems in terms of shift of plants’ phenological phases and a reduction of crop yield quantity and quality. Among the indexes used to assess plant water status, the Crop Water Stress Index (CWSI) is one of the most studied due to its ease of calculation. We performed a study in a vineyard in Trentino (San Michele all’Adige, Northern Italy) where we took advantage of IoT Technology to build a device to measure leaf temperature and automatically calculate the CWSI. Parameters necessary to determine the CWSI were the temperature of a non-transpiring leaf, (artificial 3D printed black leaf), and the temperature of a fully-transpiring leaf (wet bulb temperature of the air). We compared various types of thermometers to measure temperatures of the real leaves, and with repeated measuring campaigns performed during the summer of 2022 we could obtain spatial maps of CWSI that could highlight the stress levels of the vineyard and therefore address the irrigation management in a context of precision agriculture.

References

Adams R., Hurd B., Lenhart S., Leary N., 1998. Effects of global climate change on world agriculture: an interpretive review. Climate Research 11: 19–30.

Ahansal Y., Bouziani M., Yaagoubi R., Sebari I., Sebari K., Kenny L., 2022. Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture. Agronomy 12 (2): 297.

Apolo-Apolo O., Martínez-Guanter J., Pérez-Ruiz M., Egea G., 2020. Design and assessment of new artificial reference surfaces for real time monitoring of crop water stress index in maize. Agricultural Water Management 240: 106304.

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. Precision Agriculture 15 (4): 361–376.

Buckley T.N., 2019. How do stomata respond to water status? New Phytologist 224 (1): 21–36. Bwambale E., Abagale F.K., Anornu G.K., 2022. Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management 260: 107324.

Caffarra A. and Eccel E., 2011. Projecting the impacts of climate change on the phenology of grapevine in a mountain area: Effects of climate change on grape phenology. Australian Journal of Grape and Wine Research 17 (1): 52–61.

Change C.C. and Services A.M., 2024. Global Climate Highlights Report 2023. English.

Chaves M., Costa J., Zarrouk O., Pinheiro C., Lopes C., Pereira J., 2016. Controlling stomatal aperture in semi-arid regions—The dilemma of saving water or being cool? Plant Science 251: 54–64.

Deloire A., Pellegrino A., Rogiers S., 2020. A few words on grapevine leaf water potential. IVES Technical Reviews, vine and wine.

Esposito M., Palma L., Belli A., Sabbatini L., Pierleoni P., 2022. Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review. Sensors 22 (6): 2124.

Fuentes-Peñailillo F., Ortega-Farías S., Acevedo-Opazo C., Rivera M., Araya-Alman M., 2024. A Smart Crop Water Stress Index-Based IoT Solution for Precision Irrigation of Wine Grape. Sensors 24 (1).

Halbritter A.H., De Boeck H.J., Eycott A.E., Reinsch S., Robinson D.A., Vicca S., Berauer B., Christiansen C.T., Estiarte M., Grünzweig J.M., Gya R., Hansen K., Jentsch A., Lee H., Linder S., Marshall J., Pen˜ uelas J., Kappel Schmidt I., Stuart-Hae¨ntjens E., Wilfahrt P., the ClimMani Working Group, Vandvik V., 2020. The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx). Methods in Ecology and Evolution 11 (1). Ed. by Freckleton R.: 22–37.

Idso, 1982. Non-water-stressed baselines: A key to measuring and interpreting plant water stress. Agricultural Meteorology 27 (1-2): 59–70.

Idso, Jackson R., Pinter P., Reginato R., Hatfield J., 1981. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology 24: 45–55.

Intergovernmental Panel On Climate Change (IPCC), 2023. Climate Change 2022 – Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 1st ed. Cambridge University Press.

Jackson R.D., Idso, Reginato R.J., Pinter P.J., 1981. Canopy temperature as a crop water stress indicator. Water Resources Research 17 (4): 1133–1138.

Jayalakshmy M.S. and Philip J., 2010. Thermophysical Properties of Plant Leaves and Their Influence on the Environment Temperature. International Journal of Thermophysics 31 (11-12): 2295–2304.

Jones H.G., 1999. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agricultural and Forest Meteorology 95 (3): 139–149.

Jones H.G., Hutchinson P.A., May T., Jamali H., Deery D.M., 2018. A practical method using a network of fixed infrared sensors for estimating crop canopy conductance and evaporation rate. Biosystems Engineering 165: 59–69.

Karaka C., Tekelioglu B., Buyuktas D., Bastug R., 2018. Relations between Crop Water Stress Index and stomatal conductance of soybean depending on cultivars. Fresenius Environmental Bulletin 27. Edition: 27 Section: 6: 4212–4219.

Katimbo A., Rudnick D.R., DeJonge K.C., Lo T.H., Qiao X., Franz T.E., Nakabuye H.N., Duan J., 2022. Crop water stress index computation approaches and their sensitivity to soil water dynamics. Agricultural Water Management 266: 107575.

Kim Y., Still C.J., Roberts D.A., Goulden M.L., 2018. Thermal infrared imaging of conifer leaf temperatures: Comparison to thermocouple measurements and assessment of environmental influences. Agricultural and Forest Meteorology 248: 361–371.

King B.A., Shellie K.C., 2023. A crop water stress index based internet of things decision support system for precision irrigation of wine grape. Smart Agricultural Technology 4: 100202. Lawrence M.G., 2005. The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bulletin of the American Meteorological Society 86 (2): 225–234.

Leinonen I., Jones H.G., 2004. Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany 55 (401): 1423–1431.

Maes W.H., Baert A., Huete A.R., Minchin P.E., Snelgar W.P., Steppe K., 2016. A new wet reference target method for continuous infrared thermography of vegetations. Agricultural and Forest Meteorology 226-227: 119–131.

Mahan J.R., Conaty W., Neilsen J., Payton P., Cox S.B., 2010. Field performance in agricultural settings of a wireless temperature monitoring system based on a low-cost infrared sensor. Computers and Electronics in Agriculture 71 (2): 176–181.

Martínez J., Egea G., Agüera J., Pérez-Ruiz M., 2017. A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet. Precision Agriculture 18 (1): 95–110.

Matese A., Baraldi R., Berton A., Cesaraccio C., Di Gennaro S.F., Duce P., Facini O., Mameli M.G., Piga A., Zaldei A., 2018. Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods. Remote Sensing 10 (1): 159–167.

Matese A., Crisci A., Di Gennaro S.F., Primicerio J., Tomasi D., Marcuzzo P., Guidoni S., 2014. Spatial variability of meteorological conditions at different scales in viticulture. Agricultural and Forest Meteorology 189-190: 159–167.

Mathur S., Agrawal D., Jajoo A., 2014. Photosynthesis: Response to high temperature stress. Journal of Photochemistry and Photobiology B: Biology 137: 116–126.

Meron M., Tsipris J., Orlov V., Alchanatis V., Cohen Y., 2010. Crop water stress mapping for site- specific irrigation by thermal imagery and artificial reference surfaces. Precision Agriculture 11 (2): 148–162.

Nair S., Johnson J., Wang C., 2013. Efficiency of Irrigation Water Use: A Review from the Perspectives of Multiple Disciplines. Agronomy Journal 105 (2): 351–363.

Nanda M.K., Giri U., Bera N., 2018. Canopy Temperature-Based Water Stress Indices: Potential and Limitations. In: Advances in Crop Environment Interaction. Ed. by Bal S.K., Mukherjee J., Choudhury B.U., Dhawan A.K. Singapore: Springer Singapore, 365–385.

Ouerghi F., Ben-Hammouda M., Teixeira Da Silva J., Albouchi A., Bouzaien G., Aloui S., Cheikh- M’Hamed H., Nasraoui B., 2014. The effects of vapor gard on some physiological traits of durum wheat and barley leaves under water stress. Agriculturae Conspectus Scientificus 79 (4): 261–267.

Pallas J.E., Michel B.E., Harris D.G., 1967. Photosynthesis, Transpiration, Leaf Temperature, and Stomatal Activity of Cotton Plants under Varying Water Potentials. Plant Physiology 42 (1): 76–88.

Peña Quiñones A.J., Hoogenboom G., Salazar Gutiérrez M.R., Stöckle C., Keller M., 2020. Comparison of air temperature measured in a vineyard canopy and at a standard weather station. PLOS ONE 15 (6). Ed. by Huang M.: e0234436.

Poblete-Echeverría C., Espinace D., Sepúlveda-Reyes D., Zúñiga M., Sanchez M., 2017. Analysis of crop water stress index (CWSI) for estimating stem water potential in grapevines: comparison between natural reference and baseline approaches. Acta Horticulturae (1150): 189–194.

Poirier-Pocovi M., Bailey B.N., 2020. Sensitivity analysis of four crop water stress indices to ambient environmental conditions and stomatal conductance. Scientia Horticulturae 259: 108825.

Ru C., Hu X., Wang W., Ran H., Song T., Guo Y., 2020. Evaluation of the Crop Water Stress Index as an Indicator for the Diagnosis of Grapevine Water Deficiency in Greenhouses. Horticulturae 6 (4): 86.

Saxton K.E., Rawls W.J., 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Science Society of America Journal 70 (5): 1569–1578.

Stull R., 2011. Wet-Bulb Temperature from Relative Humidity and Air Temperature. Journal of Applied Meteorology and Climatology 50 (11): 2267–2269.

Tarnopolsky M., Seginer I., 1999. Leaf temperature error from heat conduction along thermo- couple wires. Agricultural and Forest Meteorology 93 (3): 185–194.

Venios X., Korkas E., Nisiotou A., Banilas G., 2020. Grapevine Responses to Heat Stress and Global Warming. Plants 9 (12): 1754.

Wu J., Wang J., Hui W., Zhao F., Wang P., Su C., Gong W., 2022. Physiology of Plant Responses to Water Stress and Related Genes: A Review. Forests 13 (2): 324.

Yu, Ding G.-D., Gao G.-L., Zhao Y.-Y., Yan L., Sai K., 2015. Using Plant Temperature to Evaluate the Response of Stomatal Conductance to Soil Moisture Deficit. Forests 6 (12): 3748–3762.

Yu L., Wang W., Xin Z., Zheng W., 2016. A Review on Leaf Temperature Sensor: Measurement Methods and Application. In: vol. 478.

Zhao W., Liu L., Shen Q., Yang J., Han X., Tian F., Wu J., 2020. Effects of Water Stress on Photosynthesis, Yield, and Water Use Efficiency in Winter Wheat. Water 12 (8): 2127.

Zhou Z., Diverres G., Kang C., Thapa S., Karkee M., Zhang Q., Keller M., 2022. Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season. Agronomy 12 (2): 322.

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Published

2024-12-28

How to Cite

Mattedi, C., Rodeghiero, M., & Zorer, R. (2024). IoT technology as a support tool for the calculation of Crop Water Stress Index in a Vitis vinifera L. cv. Chardonnay vineyard in Northern Italy. Italian Journal of Agrometeorology, (2), 65–80. https://doi.org/10.36253/ijam-2747

Issue

No. 2 (2024)

Section

RESEARCH ARTICLES

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Copyright (c) 2024 Cecilia Mattedi, Mirco Rodeghiero, Roberto Zorer

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