Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Short-range sensing for fruit tree water stress detection and monitoring in orchards: a review

  • Sumaiya Islam (Department of Smart Agricultural Systems, Graduate School, Chungnam National University) ;
  • Md Nasim Reza (Department of Smart Agricultural Systems, Graduate School, Chungnam National University) ;
  • Shahriar Ahmed (Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University) ;
  • Md Shaha Nur Kabir (Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University) ;
  • Sun-Ok Chung (Department of Smart Agricultural Systems, Graduate School, Chungnam National University) ;
  • Heetae Kim (National Institute of Agricultural Sciences, Rural Development Administration)
  • Received : 2023.10.06
  • Accepted : 2023.11.22
  • Published : 2023.12.01
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Abstract

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Short-range sensing can be reliable, rapid, inexpensive, and used for applications based on well-developed and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

Keywords

Acknowledgement

This work was carried out with the support of "New agricultural climate change response system establishment project (Project No. PJ014944)", Rural Development Administration, Republic of Korea.

References

  1. Aladenola O, Madramootoo C. 2014. Response of greenhouse-grown bell pepper (Capsicum annuum L.) to variable irrigation. Canadian Journal of Plant Science 94:303-310. https://doi.org/10.4141/cjps2013-048
  2. Anderson K, Gaston KJ. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11:138-146. https://doi.org/10.1890/120150
  3. Arendse E, Fawole OA, Magwaza LS, Opara UL. 2018. Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review. Journal of Food Engineering 217:11-23. https://doi.org/10.1016/j.jfoodeng.2017.08.009
  4. Bacelar EA, Santos DL, Moutinho-Pereira JM, Lopes JI, Goncalves BC, Ferreira TC, Correia CM. 2007. Physiological behaviour, oxidative damage and antioxidative protection of olive trees grown under different irrigation regimes. Plant and Soil 292:1-12. https://doi.org/10.1007/s11104-006-9088-1
  5. Baluja J, Diago MP, Balda P, Zorer R, Meggio F, Morales F, Tardaguila J. 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science 30:511-522. https://doi.org/10.1007/s00271-012-0382-9
  6. Ballester C, Jimenez-Bello MA, Castel JR, Intrigliolo DS. 2013. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agricultural and Forest Meteorology 168:120-129. https://doi.org/10.1016/j.agrformet.2012.08.005
  7. Bayat B, Van der Tol C, Verhoef W. 2016. Remote sensing of grass response to drought stress using spectroscopic techniques and canopy reflectance model inversion. Remote Sensing 8:557.
  8. Bellvert J, Adeline K, Baram S, Pierce L, Sanden BL, Smart DR. 2018. Monitoring crop evapotranspiration and crop coefficients over an almond and pistachio orchard throughout remote sensing. Remote Sensing 10:2001.
  9. Bellvert J, Marsal J, Girona J, Gonzalez-Dugo V, Fereres E, Ustin SL, Zarco-Tejada PJ. 2016. Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and Saturn peach orchards. Remote Sensing 8:39.
  10. Bellvert J, Marsal J, Girona J, Zarco-Tejada PJ. 2015. Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery. Irrigation Science 33:81-93. https://doi.org/10.1007/s00271-014-0456-y
  11. Ben-Gal A, Agam N, Alchanatis V, Cohen Y, Yermiyahu U, Zipori I, Presnov E, Sprintsin M, Dag A. 2009. Evaluating water stress in irrigated olives: Correlation of soil water status, tree water status, and thermal imagery. Irrigation Science 27:367-376. https://doi.org/10.1007/s00271-009-0150-7
  12. Boland AM, Mitchell PD, Jerie PH, Goodwin I. 1993. The effect of regulated deficit irrigation on tree water use and growth of peach. Journal of Horticultural Science 68:261-274. https://doi.org/10.1080/00221589.1993.11516351
  13. Buckley TN. 2019. How do stomata respond to water status? New Phytologist 224:21-36. https://doi.org/10.1111/nph.15899
  14. Calderon R, Navas-Cortes JA, Zarco-Tejada PJ. 2015. Early detection and quantification of Verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sensing 7: 5584-5610. https://doi.org/10.3390/rs70505584
  15. Chaerle L, Leinonen I, Jones HG, Van Der Straeten D. 2007. Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of Experimental Botany 58:773-784. https://doi.org/10.1093/jxb/erl257
  16. Cheng T, Riano D, Ustin SL. 2014. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sensing of Environment 143:39-53. https://doi.org/10.1016/j.rse.2013.11.018
  17. Cramer GR, Urano K, Delrot S, Pezzotti M, Shinozaki K. 2011. Effects of abiotic stress on plants: A systems biology perspective. BMC Plant Biology 11:163.
  18. De Bei R, Cozzolino D, Sullivan W, Cynkar W, Fuentes S, Dambergs R, Pech J, Tyerman S. 2011. Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Australian Journal of Grape and Wine Research 17:62-71. https://doi.org/10.1111/j.1755-0238.2010.00117.x
  19. Diago MP, Bellincontro A, Scheidweiler M, Tardaguila J, Tittmann S, Stoll M. 2017. Future opportunities of proximal near infrared spectroscopy approaches to determine the variability of vineyard water status. Australian Journal of Grape and Wine Research 23:409-414. https://doi.org/10.1111/ajgw.12283
  20. Diago MP, Fernandez-Novales J, Gutierrez S, Maranon M, Tardaguila J. 2018. Development and validation of a new methodology to assess the vineyard water status by on-the-go near infrared spectroscopy. Frontiers in Plant Science 9:59.
  21. Dzikiti S, Verreynne SJ, Stuckens J, Strever A, Verstraeten WW, Swennen R, Theron KI, Coppin P. 2011. Seasonal variation in canopy reflectance and its application to determine the water status and water use by citrus trees in the Western Cape, South Africa. Agricultural and Forest Meteorology 151:1035-1044. https://doi.org/10.1016/j.agrformet.2011.03.007
  22. Egea G, Padilla-Diaz CM, Martinez-Guanter J, Fernandez JE, Perez-Ruiz M. 2017. Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agricultural Water Management 187:210-221. https://doi.org/10.1016/j.agwat.2017.03.030
  23. Facini O, Loreti S, Rossi F, Bignami C. 2003. Canopy and leaf light reflectance features in relation to water content in apple. pp. 217-224. In Proceeding of ISHS Acta Horticulturae 664: IV International Symposium on Irrigation of Horticultural Crops.
  24. Fernandez-Novales J, Barrio I, Diago MP. 2021. Non-invasive monitoring of berry ripening using on-the-go hyperspectral imaging in the vineyard. Agronomy 11:2534.
  25. Fernandez-Novales J, Tardaguila J, Gutierrez S, Maranon M, Diago MP. 2018. In field quantification and discrimination of different vineyard water regimes by on-the-go NIR spectroscopy. Biosystems Engineering 165:47-58. https://doi.org/10.1016/j.biosystemseng.2017.08.018
  26. Gamon JA, Penuelas J, Field CB. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41:35-44. https://doi.org/10.1016/0034-4257(92)90059-S
  27. Garcia-Tejero IF, Hernandez A, Padilla-Diaz CM, Diaz-Espejo A, Fernandez JE. 2017. Assessing plant water status in a hedgerow olive orchard from thermography at plant level. Agricultural Water Management 188:50-60. https://doi.org/10.1016/j.agwat.2017.04.004
  28. Garcia-Tejero IF, Ortega-Arevalo CJ, Iglesias-Contreras M, Moreno JM, Souza L, Tavira SC, Duran-Zuazo VH. 2018. Assessing the crop-water status in almond (Prunus dulcis Mill.) trees via thermal imaging camera connected to smartphone. Sensors 18:1050.
  29. Gates RE. 1964. Eruption of permanent teeth of New South Wales school children. Part I. Ages of eruption. Australian Dental Journal 9:211-218.
  30. Gomez-Bellot MJ, Nortes PA, Sanchez-Blanco MJ, Ortuno MF. 2015. Sensitivity of thermal imaging and infrared thermometry to detect water status changes in Euonymus japonica plants irrigated with saline reclaimed water. Biosystems Engineering 133:21-32. https://doi.org/10.1016/j.biosystemseng.2015.02.014
  31. Gonzalez-Dugo V, Zarco-Tejada P, Berni JAJ, Suarez L, Goldhamer D, Fereres E. 2012. Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent. Agricultural and Forest Meteorology 154-155:156-165. https://doi.org/10.1016/j.agrformet.2011.11.004
  32. Gonzalez-Dugo V, Zarco-Tejada PJ, Fereres E. 2014. Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards. Agricultural and Forest Meteorology 198-199:94-104. https://doi.org/10.1016/j.agrformet.2014.08.003
  33. Gonzalez-Dugo V, Zarco-Tejada P, Nicolas E, Nortes PA, Alarcon JJ, Intrigliolo DS, Fereres E. 2013. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture 14:660-678. https://doi.org/10.1007/s11119-013-9322-9
  34. Gonzalez-Fernandez AB, Rodriguez-Perez JR, Marabel M, Alvarez-Taboada F. 2015b. Spectroscopic estimation of leaf water content in commercial vineyards using continuum removal and partial least squares regression. Scientia Horticulturae 188:15-22. https://doi.org/10.1016/j.scienta.2015.03.012
  35. Gonzalez-Fernandez AB, Rodriguez-Perez JR, Marcelo V, Valenciano JB. 2015a. Using field spectrometry and a plant probe accessory to determine leaf water content in commercial vineyards. Agricultural water management 156:43-50. https://doi.org/10.1016/j.agwat.2015.03.024
  36. Gull A, Lone AA, Wani NUI. 2019. Biotic and abiotic stresses in plants. pp. 1-19. Abiotic and Biotic Stress in Plants. IntechOpen Limited, London, UK.
  37. Gutierrez S, Diago MP, Fernandez-Novales J, Tardaguila J. 2018. Vineyard water status assessment using on-the-go thermal imaging and machine learning. PLoS One 13:e0192037.
  38. Huang Y, Ren Z, Li D, Liu X. 2020. Phenotypic techniques and applications in fruit trees: A review. Plant Methods 16:107.
  39. Ihuoma SO, Madramootoo CA. 2017. Recent advances in crop water stress detection. Computers and Electronics in Agriculture 141:267-275. https://doi.org/10.1016/j.compag.2017.07.026
  40. IPCC (Intergovernmental Panel on Climate Change). 2018. Global warming of 1.5℃. An IPCC Special Report on the impacts of global warming of 1.5℃ above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. IPCC, Geneva, Switzerland.
  41. Jackson RD, Idso SB, Reginato RJ, Pinter Jr PJ. 1981. Canopy temperature as a crop water stress indicator. Water Resources Research 17:1133-1138. https://doi.org/10.1029/WR017i004p01133
  42. James RA, Sirault XRR. 2012. Infrared thermography in plant phenotyping for salinity tolerance. Plant Salt Tolerance. Methods in Molecular Biology. pp. 173-189. Humana Press, NJ, USA.
  43. Jones HG. 2013. Plants and microclimate: A quantitative approach to environmental plant physiology. Cambridge University Press, London, UK.
  44. Kalaji HM, Baba W, Gediga K, Goltsev V, Samborska IA, Cetner MD, Dimitrova S, Piszcz U, Bielecki K, Karmowska K, et al. 2018. Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants. Photosynthesis Research 136:329-343. https://doi.org/10.1007/s11120-017-0467-7
  45. Kalyar T, Rauf S, Teixeira da Silva JA, Haidar S, Iqbal Z. 2013. Utilization of leaf temperature for the selection of leaf gas-exchange traits to induce heat resistance in sunflower (Helianthus annuus L.). Photosynthetica 51:419-428. https://doi.org/10.1007/s11099-013-0038-x
  46. Kandylakis Z, Falagas A, Karakizi C, Karantzalos K. 2020. Water stress estimation in vineyards from aerial SWIR and multispectral UAV data. Remote Sensing 12:2499.
  47. Kochhar SL, Gujral SK. 2020. Plant physiology: Theory and applications. Cambridge University Press, London, UK.
  48. Kriston-Vizi J, Umeda M, Miyamoto K. 2008. Assessment of the water status of mandarin and peach canopies using visible multispectral imagery. Biosystems Engineering 100:338-345. https://doi.org/10.1016/j.biosystemseng.2008.04.001
  49. Lodish HF, Berk A, Kaiser C, Krieger M, Bretscher A, Ploegh HL, Amon A, Martin KC. 2016. Molecular cell biology. W. H. Freeman and Company, New York, USA.
  50. Lu B, Dao PD, Liu J, He Y, Shang J. 2020. Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing 12:2659.
  51. Luo WH, Wang XH, Dai JF, Ding WM, Guo SR, Li SJ. 2004. Measurement and simulation of cucumber canopy transpiration in a subtropical modern greenhouse under winter climate conditions. Chinese Journal of Plant Ecology 28:59-65.
  52. Maniwara P, Nakano K, Boonyakiat D, Ohashi S, Hiroi M, Tohyama T. 2014. The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality. Journal of Food Engineering 143:33-43. https://doi.org/10.1016/j.jfoodeng.2014.06.028
  53. Matese A, Baraldi R, Berton A, Cesaraccio C, Di Gennaro SF, Duce P, Facini O, Mameli MG, Piga A, Zaldei A. 2018. Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sensing 10:114.
  54. Meola C, Carlomagno GM. 2004. Recent advances in the use of infrared thermography. Measurement Science and Technology 15:R27.
  55. Meron M, Sprintsin M, Tsipris J, Alchanatis V, Cohen Y. 2013. Foliage temperature extraction from thermal imagery for crop water stress determination. Precision Agriculture 14:467-477. https://doi.org/10.1007/s11119-013-9310-0
  56. Odi-Lara M, Campos I, Neale CMU, Ortega-Farias S, Poblete-Echeverria C, Balbontin C, Calera A. 2016. Estimating evapotranspiration of an apple orchard using a remote sensing-based soil water balance. Remote Sensing 8:253.
  57. Olsen JL, Ceccato P, Proud SR, Fensholt R, Grippa M, Mougin E, Ardo J, Sandholt I. 2013. Relation between seasonally detrended shortwave infrared reflectance data and land surface moisture in semi‑arid Sahel. Remote Sensing 5:2898-2927. https://doi.org/10.3390/rs5062898
  58. Osakabe Y, Osakabe K, Shinozaki K, Tran LSP. 2014. Response of plants to water stress. Frontiers in Plant Science 5:86.
  59. Osroosh Y, Peters RT, Campbell CS. 2016. Daylight crop water stress index for continuous monitoring of water status in apple trees. Irrigation Science 34:209-219. https://doi.org/10.1007/s00271-016-0499-3
  60. Paco TA, Pocas I, Cunha M, Silvestre JC, Santos FL, Paredes P, Pereira LS. 2014. Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations. Journal of Hydrology 519:2067-2080. https://doi.org/10.1016/j.jhydrol.2014.09.075
  61. Park S, Ryu D, Fuentes S, Chung H, Hernandez-Montes E, O'Connell M. 2017. Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV). Remote Sensing 9:828.
  62. Parkash V, Singh S. 2020. A review on potential plant-based water stress indicators for vegetable crops. Sustainability 12:3945.
  63. Patumi M, d'Andria R, Marsilio V, Fontanazza G, Morelli G, Lanza B. 2002. Olive and olive oil quality after intensive monocone olive growing (Olea europaea L., cv. Kalamata) in different irrigation regimes. Food Chemistry 77:27-34. https://doi.org/10.1016/S0308-8146(01)00317-X
  64. Petrie PR, Wang Y, Liu S, Lam S, Whitty MA, Skewes MA. 2019. The accuracy and utility of a low cost thermal camera and smartphone-based system to assess grapevine water status. Biosystems Engineering 179:126-139. https://doi.org/10.1016/j.biosystemseng.2019.01.002
  65. Pichon L, Brunel G, Payan JC, Taylor J, Bellon-Maurel V, Tisseyre B. 2021. ApeX-Vigne: Experiences in monitoring vine water status from within-field to regional scales using crowdsourcing data from a free mobile phone application. Precision Agriculture 22:608-626. https://doi.org/10.1007/s11119-021-09797-9
  66. Pierantozzi P, Torres M, Bodoira R, Maestri D. 2013. Water relations, biochemical -physiological and yield responses of olive trees (Olea europaea L. cvs. Arbequina and Manzanilla) under drought stress during the pre-flowering and flowering period. Agricultural Water Management 125:13-25. https://doi.org/10.1016/j.agwat.2013.04.003
  67. Pocas I, Rodrigues A, Goncalves S, Costa PM, Goncalves I, Pereira LS, Cunha M. 2015. Predicting grapevine water status based on hyperspectral reflectance vegetation indices. Remote sensing 7:16460-16479. https://doi.org/10.3390/rs71215835
  68. Pou A, Diago MP, Medrano H, Baluja J, Tardaguila J. 2014. Validation of thermal indices for water status identification in grapevine. Agricultural Water Management 134:60-72. https://doi.org/10.1016/j.agwat.2013.11.010
  69. Quebrajo L, Perez-Ruiz M, Perez-Urrestarazu L, Martinez G, Egea G. 2018. Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosystems Engineering 165:77-87. https://doi.org/10.1016/j.biosystemseng.2017.08.013
  70. Rallo G, Minacapilli M, Ciraolo G, Provenzano G. 2014. Detecting crop water status in mature olive groves using vegetation spectral measurements. Biosystems Engineering 128:52-68. https://doi.org/10.1016/j.biosystemseng.2014.08.012
  71. Romero M, Luo Y, Su B, Fuentes S. 2018. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture 147:109-117. https://doi.org/10.1016/j.compag.2018.02.013
  72. Romero-Trigueros C, Bayona Gambin JM, Nortes Tortosa PA, Alarcon Cabanero JJ, Nicolas 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 11:757.
  73. Salgadoe ASA, Robson AJ, Lamb DW, Schneider D. 2019. A non-reference temperature histogram method for determining Tc from ground-based thermal imagery of orchard tree canopies. Remote Sensing 11:714.
  74. Santesteban LG, Di Gennaro SF, Herrero-Langreo A, Miranda C, Royo JB, Matese A. 2017. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management 183:49-59. https://doi.org/10.1016/j.agwat.2016.08.026
  75. Saradadevi R, Palta JA, Siddique KHM. 2017. ABA-mediated stomatal response in regulating water use during the development of terminal drought in wheat. Frontiers in Plant Science 8:1251.
  76. Seo MH, Tilahun S, Park DS, Melaku A, Jeong CS. 2018. Effect of ripening conditions on the quality and storability of muskmelon (Cucumis melo L.) fruits. Horticultural Science and Technology 36:741-755. https://doi.org/10.7235/HORT.20180073
  77. Sepulcre-Canto G, Zarco-Tejada PJ, Jimenez-Munoz JC, Sobrino JA, de Miguel E, Villalobos FJ. 2006. Detection of water stress in an olive orchard with thermal remote sensing imagery. Agricultural and Forest Meteorology 136:31-44. https://doi.org/10.1016/j.agrformet.2006.01.008
  78. Serrano L, Gonzalez-Flor C, Gorchs G. 2012. Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards. Remote Sensing of Environment 118:249-258. https://doi.org/10.1016/j.rse.2011.11.021
  79. Stagakis S, Gonzalez-Dugo V, Cid P, Guillen-Climent ML, Zarco-Tejada PJ. 2012. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing 71:47-61. https://doi.org/10.1016/j.isprsjprs.2012.05.003
  80. Struthers R, Ivanova A, Tits L, Swennen R, Coppin P. 2015. Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees. International Journal of Applied Earth Observation and Geoinformation 39:9-17. https://doi.org/10.1016/j.jag.2015.02.006
  81. Suarez L, Zarco-Tejada PJ, Berni JAJ, Gonzalez-Dugo V, Fereres E. 2009. Modelling PRI for water stress detection using radiative transfer models. Remote Sensing of Environment 113:730-744. https://doi.org/10.1016/j.rse.2008.12.001
  82. Tardaguila J, Fernandez-Novales J, Gutierrez S, Diago MP. 2017. Non-destructive assessment of grapevine water status in the field using a portable NIR spectrophotometer. Journal of the Science of Food and Agriculture 97:3772-3780. https://doi.org/10.1002/jsfa.8241
  83. Tardieu F, Granier C, Muller B. 2011. Water deficit and growth. Co-ordinating processes without an orchestrator? Current Opinion in Plant Biology 14:283-289. https://doi.org/10.1016/j.pbi.2011.02.002
  84. Testi L, Goldhamer DA, 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
  85. UN (United Nations). 2015. World population prospects: The 2015 revision, key findings and advance tables. Working Paper No. ESA/P/WP.241. UN Department of Economic and Social Affairs, Population Division, New York, USA.
  86. Vijitha R, Mahendran S. 2012. Effect of moisture stress at different growth stages of tomato plant (Lycopersicon esculentum Mill.) on yield and quality of fruits. Journal of Science of the University of Kelaniya 5:1-11. https://doi.org/10.4038/josuk.v5i0.4086
  87. Xie L, Wang A, Xu H, Fu X, Ying Y. 2016. Applications of near-infrared systems for quality evaluation of fruits: A review. Transactions of the ASABE 59:399-419. https://doi.org/10.13031/trans.59.10655
  88. Yu L, Wang W, Zhang X, Zheng W. 2016. A review on leaf temperature sensor: Measurement methods and application. IFIP Advances in Information and Communication Technology 478:216-230. https://doi.org/10.1007/978-3-319-48357-3_21
  89. Zahir SADM, Omar AF, Jamlos MF, Azmi MAM, Muncan J. 2022. A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection. Sensors and Actuators A: Physical 338:113468.
  90. Zarco-Tejada PJ, Berni JAJ, Suarez L, Sepulcre-Canto G, Morales F, Miller JR. 2009. Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection. Remote Sensing of Environment 113:1262-1275. https://doi.org/10.1016/j.rse.2009.02.016
  91. Zarco-Tejada PJ, Gonzalez-Dugo V, Williams LE, Suarez L, Berni JAJ, Goldhamer D, Fereres E. 2013. A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sensing of Environment 138:38-50. https://doi.org/10.1016/j.rse.2013.07.024
  92. Zhang Q, Li Q, Zhang G. 2012. Rapid determination of leaf water content using VIS/NIR spectroscopy analysis with wavelength selection. Spectroscopy: An International Journal 27:93-105. https://doi.org/10.1155/2012/276795
  93. Zhang YJ, Hou MY, Xue HY, Liu LT, Sun HC, Li CD, Dong XJ. 2018. Photochemical reflectance index and solar-induced fluorescence for assessing cotton photosynthesis under water-deficit stress. Biologia Plantarum 62:817-825. https://doi.org/10.1007/s10535-018-0821-4
  94. 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 12:322.
  95. Zingaretti SM, Inacio MC, de Matos Pereira L, Paz TA, de Castro Franca S. 2013. Water stress and agriculture. Responses of Organisms to Water Stress. IntechOpen Limited, London, UK.
  96. Zuniga CE, Khot LR, Jacoby P, Sankaran S. 2016. Remote sensing based water-use efficiency evaluation in subsurface irrigated wine grape vines. pp. 128-134. In Proceeding of Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping 9866.