A Review of Recent Advances in Water Vapor Deficit Sensor Technology for Improving Plant Water Usage Efficiency

Document Type : Review article

Authors

1 Department of Agrotechnology, Faculty of Agricultural Technology, University of Tehran, Tehran, Iran.

2 Department of Biotechnology, Iranian Research Organization for Science and Technology (IROST), P. O. Box 3353-5111, Tehran, Iran.

Abstract

Accurate assessment and monitoring of plant water stress are essential for optimizing irrigation strategies, improving water use efficiency. This article explores the multifaceted issue of water stress, encompassing both agricultural and environmental contexts. It emphasizes the pivotal role of precise water stress detection in effectively managing water resources and fostering sustainable agricultural practices. The primary focus is on the progression of sensors designed specifically to detect water stress, with particular attention given to two approaches: Vapor Pressure Deficit (VPD) and Crop Water Stress Index (CWSI). The article thoroughly investigates the underlying principles, operational mechanisms, advantages, and limitations of these sensor technologies. It vividly showcases their wide-ranging applications across agriculture, horticulture, and environmental monitoring, elucidating their significance in each domain. Moreover, it delves into the integration of VPD and CWSI sensors and introduces emerging technologies like thermal imaging and chlorophyll fluorescence sensors, expanding the horizon of water stress detection methodologies. Addressing the challenges linked to calibration and data interpretation, the article proposes potential pathways for future research endeavors. In essence, the overarching goal of this article is to propel the development of advanced sensor technologies, ultimately facilitating precise water stress detection. It aims to bolster sustainable water resource management practices while fortifying resilient agricultural methods in the face of evolving environmental challenges. VPD and CWSI-based approaches offer precise water stress insights in agriculture, aiding irrigation management.

Keywords

Main Subjects

References

REFERENCES

Alahacoon, N., & Edirisinghe, M. (2022). A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale. Geomatics, Natural Hazards Risk, 13(1), 762-799. https://doi.org/10.1080/19475705.2022.2044394.
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, 300(9), D05109.
Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of irrigation drainage engineering, 133(4), 380-394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395).
Bajgiran, P. R., Darvishsefat, A. A., Khalili, A., & Makhdoum, M. F. (2008). Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments, 72(6), 1086-1096. https://doi.org/10.1016/j.jaridenv.2007.12.004.
Benyahia, F., Bastos Campos, F., Ben Abdelkader, A., Basile, B., Tagliavini, M., Andreotti, C., & Zanotelli, D. (2023). Assessing Grapevine Water Status by Integrating Vine Transpiration, Leaf Gas Exchanges, Chlorophyll Fluorescence and Sap Flow Measurements. Agronomy, 13(2), 464. https://doi.org/10.3390/agronomy13020464.
Beslity, J., & Shaw, S. B. (2023). Testing of a custom, portable drill press to minimize probe misalignment in sap flow sensors. Tree Physiology, tpad049. https://doi.org/10.1093/treephys/tpad049.
Dhillon, R., Udompetaikul, V., Rojo, F., Roach, J., Upadhyaya, S., Slaughter, D., Lampinen, B., & Shackel, K. (2014). Detection of plant water stress using leaf temperature and microclimatic measurements in almond, walnut, and grape crops. Transactions of the ASABE, 57(1), 297-304. doi:https://doi.org/10.13031/trans.57.10319.
Dukat, P., Ziemblińska, K., Räsänen, M., Vesala, T., Olejnik, J., & Urbaniak, M. (2023). Scots pine responses to drought investigated with eddy covariance and sap flow methods. European Journal of Forest Research, 142(3), 671-690. https://doi.org/10.1007/s10342-023-01549-w.
Elbeltagi, A., Srivastava, A., Deng, J., Li, Z., Raza, A., Khadke, L., Yu, Z., & El-Rawy, M. (2023). Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments. Agricultural Water Management, 283, 108302. doi:https://doi.org/10.1016/j.agwat.2023.108302.
Farhan, I. A., & Al-Bakri, J. (2019). Detection of a real time remote sensing indices and soil moisture for drought monitoring and assessment in Jordan. Open Journal of Geology, 9(13), 1048-1068. https://doi.org/10.4236/ojg.2019.913105.
González-Dugo, M., Moran, M., Mateos, L., & Bryant, R. (2006). Canopy temperature variability as an indicator of crop water stress severity. Irrigation science, 24(4), 233-240. https://doi.org/10.1007/s00271-005-0022-8.
Grossiord, C., Buckley, T. N., Cernusak, L. A., Novick, K. A., Poulter, B., Siegwolf, R. T., Sperry, J. S., & McDowell, N. G. (2020). Plant responses to rising vapor pressure deficit. New Phytologist, 226(6), 1550-1566. doi:https://doi.org/10.1111/nph.16485.
Han, M., Zhang, H., DeJonge, K. C., Comas, L. H., & Gleason, S. (2018). Comparison of three crop water stress index models with sap flow measurements in maize. Agricultural Water Management, 203, 366-375. https://doi.org/10.1016/j.agwat.2018.02.030.
Idso, S. B. (1982). Non-water-stressed baselines: A key to measuring and interpreting plant water stress. Agricultural Meteorology, 27(1-2), 59-70. doi:https://doi.org/10.1016/0002-1571(82)90020-6.
Idso, S. B., Jackson, R., Pinter Jr, P., Reginato, R., & Hatfield, J. (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24, 45-55. https://doi.org/10.1016/0002-1571(81)90032-7.
Jackson, R. D., Idso, S., Reginato, R., & Pinter Jr, P. (1981). Canopy temperature as a crop water stress indicator. Water resources research, 17(4), 1133-1138. https://doi.org/10.1029/WR017i004p01133.
Jackson, R. D., Kustas, W. P., & Choudhury, B. (1988). A reexamination of the crop water stress index. Irrigation science, 9, 309-317. doi:https://doi.org/10.1007/BF00296705.
Jones, H. G. (2004). Irrigation scheduling: advantages and pitfalls of plant-based methods. Journal of experimental botany, 55(407), 2427-2436. https://doi.org/10.1093/jxb/erh213.
Kizer, E. E., Upadhyaya, S. K., Ko-Madden, C. T., Drechsler, K. M., Meyers, J. N., Rojo, F. E., Schramm, A. E., & Zhang, Q. S. (2017). Continuous, proximal leaf monitoring system to assist with precision irrigation implementation using a wireless mesh network of sensors and controllers in almonds. Paper presented at the 2017 ASABE Annual International Meeting. https://doi.org/10.13031/aim.201701094.
Lee, C.-Y., & Lee, G.-B. (2005). Humidity sensors: a review. Sensor Letters, 3(1-2), 1-15. https://doi.org/10.1166/sl.2005.001.
Lei, Y., Zhang, H., Chen, F., & Zhang, L. J. S. o. t. t. E. (2016). How rural land use management facilitates drought risk adaptation in a changing climate—A case study in arid northern China. Science of the total Environment, 550, 192-199. https://doi.org/10.1016/j.scitotenv.2016.01.098.
O'Toole, J., & Real, J. (1986). Estimation of Aerodynamic and Crop Resistances from Canopy Temperature 1. Agronomy journal, 78(2), 305-310. doi:https://doi.org/10.2134/agronj1986.00021962007800020019x.
Park, S., Ryu, D., Fuentes, S., Chung, H., O’connell, M., & Kim, J. (2021). Dependence of CWSI-based plant water stress estimation with diurnal acquisition times in a nectarine orchard. Remote Sensing, 13(14), 2775. https://doi.org/10.3390/rs13142775.
Paulo, R. L. d., Garcia, A. P., Umezu, C. K., Camargo, A. P. d., Soares, F. T., & Albiero, D. (2023). Water Stress Index Detection Using a Low-Cost Infrared Sensor and Excess Green Image Processing. Sensors, 23(3), 1318. doi:https://doi.org/10.3390/s23031318.
Romero-Trigueros, C., Bayona Gambín, J. M., Nortes Tortosa, P. A., Alarcón Cabañero, J. J., & Nicolás Nicolás, E. (2019). Determination of crop water stress index by infrared thermometry in grapefruit trees irrigated with saline reclaimed water combined with deficit irrigation. Remote Sensing, 11(7), 757. https://doi.org/10.3390/rs11070757.
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. https://doi.org/10.3390/horticulturae6040086.
Senay, G. B., Bohms, S., Singh, R. K., Gowda, P. H., Velpuri, N. M., Alemu, H., & Verdin, J. P. (2013). Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach. JAWRA Journal of the American Water Resources Association, 49(3), 577-591. https://doi.org/10.1111/jawr.12057.
Shekoofa, A., Sinclair, T. R., Messina, C. D., & Cooper, M. (2016). Variation among maize hybrids in response to high vapor pressure deficit at high temperatures. Crop Science, 56(1), 392-396. doi:https://doi.org/10.2135/cropsci2015.02.0134.
Sinclair, T. R., Devi, J., Shekoofa, A., Choudhary, S., Sadok, W., Vadez, V., Riar, M., & Rufty, T. (2017). Limited-transpiration response to high vapor pressure deficit in crop species. Plant Science, 260, 109-118. doi:https://doi.org/10.1016/j.plantsci.2017.04.007.
Tang, Z., Jin, Y., Brown, P. H., & Park, M. (2023). Estimation of tomato water status with photochemical reflectance index and machine learning: Assessment from proximal sensors and UAV imagery. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1057733.
Testi, L., Goldhamer, D., Iniesta, F., & Salinas, M. (2008). Crop water stress index is a sensitive water stress indicator in pistachio trees. Irrigation science, 26, 395-405. https://doi.org/10.1007/s00271-008-0104-5.
Wang, Y., Liu, Z., Xiemuxiding, A., Zhang, X., Duan, L., & Li, R. J. J. o. P. G. R. (2023). Fulvic acid, brassinolide, and uniconazole mediated regulation of morphological and physiological traits in maize seedlings under water stress. 42(3), 1762-1774. https://doi.org/10.1007/s00344-022-10658-6.
Yin, S., Ibrahim, H., Schnable, P. S., Castellano, M. J., & Dong, L. (2021). A Field‐Deployable, Wearable Leaf Sensor for Continuous Monitoring of Vapor‐Pressure Deficit. Advanced Materials Technologies, 6(6), 2001246. https://doi.org/10.1002/admt.202001246.
Zhang, L., Zhang, H., Zhu, Q., & Niu, Y. (2023). Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value. Agricultural Water Management, 285, 108349. https://doi.org/10.1016/j.agwat.2023.108349.
Zhou, Z., Majeed, Y., Naranjo, G. D., & Gambacorta, E. M. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers Electronics in Agriculture, 182, 106019. https://doi.org/10.1016/j.compag.2021.106019.
Biomechanism and Bioenergy Research
Volume 2, Issue 2
December 2023
Pages 65-86

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  • Receive Date: 10 December 2023
  • Revise Date: 20 December 2023
  • Accept Date: 20 December 2023

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