Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance
Abstract
:1. Introduction
2. Materials and Methods
2.1. General Description
2.2. Experimental Trial and Field Measurements
2.3. Thermal Images Post-Processing and CWSI Computations
2.4. Statistical Analysis
3. Results
3.1. Hourly Evolution of Weather Conditions
3.2. Relationships among Raw Data
3.3. Seasonal Behavior of Physiological Indicators: Stress–Recovery Cycles
3.4. Comparisons Between High- and Low-Resolution Thermal Images
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Lezzoni, A.; Puławska, J.; Lang, G. Cherries: Botany, Production and Uses; Quero-Garcia, J., Ed.; CABI Pub.: Boston, MA, USA, 2017; ISBN 978 1 78064 837 8. [Google Scholar]
- Csihon, Á.; Bicskei, D.; Dremák, P.; Gonda, I. Performance of sweet cherry cultivars grafted on Colt rootstock. Int. J. Hortic. Sci. 2018, 24, 7–10. [Google Scholar] [CrossRef]
- Chai, Q.; Gan, Y.; Zhao, C.; Xu, H.-L.; Waskom, R.M.; Niu, Y.; Siddique, K.H.M. Regulated deficit irrigation for crop production under drought stress. A review. Agron. Sustain. Dev. 2016, 36, 3. [Google Scholar] [CrossRef] [Green Version]
- Ortega-Farias, S.; Fereres, E.; Sadras, V.O. Special issue on water management in grapevines. Irrig. Sci. 2012, 30, 335–337. [Google Scholar] [CrossRef] [Green Version]
- Fernandes-Silva, A.; Oliveira, M.; Paço, T.A.; Ferreira, I. Deficit irrigation in Mediterranean fruit trees and grapevines: Water stress indicators and crop responses. In Irrigation in Agroecosystems; IntechOpen: London, UK, 2018. [Google Scholar]
- Fernández, J.E.; Cuevas, M.V. Irrigation scheduling from stem diameter variations: A review. Agric. For. Meteorol. 2010, 150, 135–151. [Google Scholar] [CrossRef]
- Johnson, R.S.; Handley, D.F.; Day, K.R. Postharvest water stress of an early maturing plum. J. Hortic. Sci. 1994, 69, 1035–1041. [Google Scholar] [CrossRef]
- Blanco, V.; Domingo, R.; Pérez-Pastor, A.; Blaya-Ros, P.J.; Torres-Sánchez, R. Soil and plant water indicators for deficit irrigation management of field-grown sweet cherry trees. Agric. Water Manag. 2018, 208, 83–94. [Google Scholar] [CrossRef]
- Geerts, S.; Raes, D. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agric. Water Manag. 2009, 96, 1275–1284. [Google Scholar] [CrossRef] [Green Version]
- Marsal, J.; Lopez, G.; Campo, J.; del Mata, M.; Arbones, A.; Girona, J. Postharvest regulated deficit irrigation in ‘Summit’ sweet cherry: Fruit yield and quality in the following season. Irrig. Sci. 2010, 28, 181–189. [Google Scholar] [CrossRef]
- Rieger, M.; Duemmel, M.J. Comparison of drought resistance among Prunus species from divergent habitats. Tree Physiol. 1992, 11, 369–380. [Google Scholar] [CrossRef]
- Carrasco-Benavides, M.; Espinoza Meza, S.; Olguín-Cáceres, J.; Muñoz-Concha, D.; von Bennewitz, E.; Ávila-Sánchez, C.; Ortega-Farías, S. Effects of regulated post-harvest irrigation strategies on yield, fruit quality and water productivity in a drip-irrigated cherry orchard. N. Z. J. Crop Hortic. 2020, 48, 1–20. [Google Scholar] [CrossRef]
- Fuentes, S.; Bei, R.D.; Pech, J.; Tyerman, S. Computational water stress indices obtained from thermal image analysis of grapevine canopies. Irrig. Sci. 2012, 30, 523–536. [Google Scholar] [CrossRef]
- Livellara, N.; Saavedra, F.; Salgado, E. Plant based indicators for irrigation scheduling in young cherry trees. Agric. Water Manag. 2011, 98, 684–690. [Google Scholar] [CrossRef]
- García-Tejero, I.F.; Gutiérrez-Gordillo, S.; Ortega-Arévalo, C.; Iglesias-Contreras, M.; Moreno, J.M.; Souza-Ferreira, L.; Durán-Zuazo, V.H. Thermal imaging to monitor the crop-water status in almonds by using the non-water stress baselines. Sci. Hortic. 2018, 238, 91–97. [Google Scholar] [CrossRef]
- Gonzalez-Dugo, V.; Zarco-Tejada, P.J.; Fereres, E. Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards. Agric. For. Meteorol. 2014, 198–199, 94–104. [Google Scholar] [CrossRef]
- Jones, H.G.; Leinonen, I. Thermal imaging for the study of plant water relations. J. Agric. Metereol. 2003, 59, 205–217. [Google Scholar] [CrossRef] [Green Version]
- Möller, M.; Alchanatis, V.; Cohen, Y.; Meron, M.; Tsipris, J.; Naor, A.; Ostrovsky, V.; Sprintsin, M.; Cohen, S. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J. Exp. Bot. 2007, 58, 827–838. [Google Scholar] [CrossRef] [Green Version]
- Sepúlveda-Reyes, D.; Ingram, B.; Bardeen, M.; Zúñiga, M.; Ortega-Farías, S.; Poblete-Echeverría, C. Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging. Remote Sens. 2016, 8, 822. [Google Scholar] [CrossRef] [Green Version]
- Testi, L.; Goldhamer, D.A.; Iniesta, F.; Salinas, M. Crop water stress index is a sensitive water stress indicator in pistachio trees. Irrig. Sci. 2008, 26, 395–405. [Google Scholar] [CrossRef] [Green Version]
- Agam, N.; Cohen, Y.; Berni, J.A.J.; Alchanatis, V.; Kool, D.; Dag, A.; Yermiyahu, U.; Ben-Gal, A. An insight to the performance of crop water stress index for olive trees. Agric. Water Manag. 2013, 118, 79–86. [Google Scholar] [CrossRef]
- Idso, S.B.; Jackson, R.D.; Pinter, P.J.; Reginato, R.J.; Hatfield, J.L. Normalizing the stress-degree-day parameter for environmental variability. Agric. Meteorol. 1981, 24, 45–55. [Google Scholar] [CrossRef]
- Jones, H.G.; Stoll, M.; Santos, T.; Sousa, C.; de Chaves, M.M.; Grant, O.M. Use of infrared thermography for monitoring stomatal closure in the field: Application to grapevine. J. Exp. Bot. 2002, 53, 2249–2260. [Google Scholar] [CrossRef] [PubMed]
- O’Shaughnessy, S.A.; Evett, S.R.; Colaizzi, P.D.; Howell, T.A. Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agric. Water Manag. 2011, 98, 1523–1535. [Google Scholar] [CrossRef]
- Gade, R.; Moeslund, T.B. Thermal cameras and applications: A survey. Mach. Vis. Appl. 2014, 25, 245–262. [Google Scholar] [CrossRef] [Green Version]
- Fuentes, S.; Poblete-Echeverria, C.; Lobos, G.; Collmann, R. Size does not matter for infrared: Water status assessment: Newly-developed infrared scanners could offer comparable results against high-resolution thermal cameras. Wine Vitic. J. 2014, 29, 45. [Google Scholar]
- Petrie, P.R.; Wang, Y.; Liu, S.; Lam, S.; Whitty, M.A.; Skewes, M.A. The accuracy and utility of a low cost thermal camera and smartphone-based system to assess grapevine water status. Biosyst. Eng. 2019, 179, 126–139. [Google Scholar] [CrossRef]
- Poblete, T.; Ortega-Farias, S.; Ryu, D. Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard. Sensors 2018, 18, 397. [Google Scholar] [CrossRef] [Green Version]
- García-Tejero, I.F.; Rubio, A.E.; Viñuela, I.; Hernández, A.; Gutiérrez-Gordillo, S.; Rodríguez-Pleguezuelo, C.R.; Durán-Zuazo, V.H. Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies. Agric. Water Manag. 2018, 208, 176–186. [Google Scholar] [CrossRef]
- García-Tejero, I.F.; Ortega-Arévalo, C.J.; Iglesias-Contreras, M.; Moreno, J.M.; Souza, L.; Tavira, S.C.; Durán-Zuazo, V.H. Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone. Sensors 2018, 18, 1050. [Google Scholar] [CrossRef] [Green Version]
- Santibañez, F.; Santibañez, P.; Caroca, C. Atlas Agroclimático de Chile. Fundación para la Innov. Agraria—FIA, Ministerio de Agricultura; Centro de Agricultura y Medioambiente—AGRIMED, Universidad de Chile, Santiago, Chile 2017. Available online: http://www.agrimed.cl/atlas/tomo3.html (accessed on 10 April 2020).
- CIREN. Estudio agrológico VII Región. In Descripciones de suelos. Materiales y Símbolos; CIREN: Santiago, Chile, 1997; Volume I. [Google Scholar]
- Saxton, K.E.; Rawls, W.J. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Sci. Soc. Am. J. 2006, 70, 1569–1578. [Google Scholar] [CrossRef] [Green Version]
- Carrasco-Benavides, M.; Mora, M.; Maldonado, G.; Olguín-Cáceres, J.; von Bennewitz, E.; Ortega-Farías, S.; Gajardo, J.; Fuentes, S. Assessment of an automated digital method to estimate leaf area index (LAI) in cherry trees. N. Z. J. Crop Hortic. 2016, 44, 247–261. [Google Scholar] [CrossRef]
- Podestá, L.; Vallone, R.; Sánchez, E.; Morábito, J.A. Effect of water deficit irrigation on vegetative growth of young cherry trees (Prunus avium L.). Rev. Fac. Cienc. Agrar. Univ. Nac. Cuyo. 2010, 42, 73–91. [Google Scholar]
- Akkuzu, E.; Kaya, Ü.; Çamoğlu, G.; Mengü, G.P.; Aşik, Ş. Determination of Crop Water Stress Index and Irrigation Timing on Olive Trees Using a Handheld Infrared Thermometer. J. Irrig. Drain. Eng. 2013, 139, 728–737. [Google Scholar] [CrossRef] [Green Version]
- Bozkurt, Y.; Yazar, A. Evaluation of crop water stress index on Royal table grape variety under partial root drying and conventional deficit irrigation regimes in the Mediterranean Region. Sci. Hortic. 2017, 224, 384–394. [Google Scholar] [CrossRef]
- Gelly, M.; Recasens, I.; Mata, M.; Arbones, A.; Rufat, J.; Girona, J.; Marsal, J. Effects of water deficit during stage II of peach fruit development and postharvest on fruit quality and ethylene production. J. Hortic. Sci. Biotechnol. 2003, 78, 324–330. [Google Scholar] [CrossRef]
- Jones, H.G. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric. For. Meteorol. 1999, 95, 139–149. [Google Scholar] [CrossRef]
- Martin, R.F. General Deming regression for estimating systematic bias and its confidence interval in Method-Comparison studies. Clin. Chem. 2000, 46, 100–104. [Google Scholar] [CrossRef]
- Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Corr. 3rd printing; Springer: New York, NY, USA, 2010; ISBN 978-0-387-98140-6. [Google Scholar]
- Poirier-Pocovi, M.; Bailey, B.N. Sensitivity analysis of four crop water stress indices to ambient environmental conditions and stomatal conductance. Sci. Hortic. 2020, 259, 108825. [Google Scholar] [CrossRef]
- Guilioni, L.; Jones, H.G.; Leinonen, I.; Lhomme, J.P. On the relationships between stomatal resistance and leaf temperatures in thermography. Agric. For. Meteorol. 2008, 148, 1908–1912. [Google Scholar] [CrossRef]
- Ahumada-Orellana, L.; Ortega-Farías, S.; Poblete-Echeverría, C.; Searles, P.S. Estimation of stomatal conductance and stem water potential threshold values for water stress in olive trees (cv. Arbequina). Irrig. Sci. 2019, 37, 461–467. [Google Scholar] [CrossRef]
- Ben-Gal, A.; Agam, N.; Alchanatis, V.; Cohen, Y.; Yermiyahu, U.; Zipori, I.; Presnov, E.; Sprintsin, M.; Dag, A. Evaluating water stress in irrigated olives: Correlation of soil water status, tree water status, and thermal imagery. Irrig. Sci. 2009, 27, 367–376. [Google Scholar] [CrossRef]
- Naor, A. Relations between leaf and stem water potentials and stomatal conductance in three field-grown woody species. J. Hortic. Sci. Biotechnol. 1998, 73, 431–436. [Google Scholar] [CrossRef]
- Struthers, R.; Ivanova, A.; Tits, L.; Swennen, R.; Coppin, P. Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees. Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 9–17. [Google Scholar] [CrossRef]
- Belfiore, N.; Vinti, R.; Lovat, L.; Chitarra, W.; Tomasi, D.; de Bei, R.; Meggio, F.; Gaiotti, F. Infrared thermography to estimate vine water status: Optimizing canopy measurements and thermal indices for the varieties Merlot and Moscato in northern Italy. Agronomy 2019, 9, 821. [Google Scholar] [CrossRef] [Green Version]
- Egea, G.; Padilla-Díaz, C.M.; Martinez-Guanter, J.; Fernández, J.E.; Pérez-Ruiz, M. Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agric. Water Manag. 2017, 187, 210–221. [Google Scholar] [CrossRef] [Green Version]
- Poblete-Echeverría, C.; Ortega-Farías, S.; Zuñiga, M.; Lobos, G.A.; Romero, S.; Estrada, F.; Fuentes, S. Use of infrared thermography on canopies as indicator of water stress in ‘Arbequina’ olive orchards. Acta Hortic. 2014, 1057, 399–403. [Google Scholar] [CrossRef]
- Stöckl, D.; Dewitte, K.; Thienpont, L.M. Validity of linear regression in method comparison studies: Is it limited by the statistical model or the quality of the analytical input data? Clin. Chem. 1998, 44, 2340–2346. [Google Scholar] [CrossRef] [Green Version]
- Klaessens, J.H.; Veen, A.; van der Verdaasdonk, R.M. Comparison of the temperature accuracy between smart phone based and high-end thermal cameras using a temperature gradient phantom. In Proceedings of the Design and Quality for Biomedical Technologies X, San Francisco, CA, USA; 2017; Volume 10056, p. 100560D. [Google Scholar]
Cultivar | Growing Season | Variable | Test of Linearity * | Intercept (a) ** | t-Test | Slope (b) ** | t-Test |
---|---|---|---|---|---|---|---|
‘Regina’ | 2017–2018 | Tc | T | 3.30 | F | 0.64 | F |
2018–2019 | F | −10.26 | F | 1.10 | F | ||
2017–2018 | CWSI | T | −0.21 | T | 1.26 | F | |
2018–2019 | T | 0.07 | T | 0.72 | F | ||
‘Sweetheart’ | 2017–2018 | Tc | T | −1.84 | T | 0.81 | F |
2018–2019 | F | −1.44 | T | 0.82 | F | ||
2017–2018 | CWSI | T | −0.08 | T | 0.91 | F | |
2018–2019 | F | 0.10 | F | 0.67 | F |
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Carrasco-Benavides, M.; Antunez-Quilobrán, J.; Baffico-Hernández, A.; Ávila-Sánchez, C.; Ortega-Farías, S.; Espinoza, S.; Gajardo, J.; Mora, M.; Fuentes, S. Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance. Sensors 2020, 20, 3596. https://doi.org/10.3390/s20123596
Carrasco-Benavides M, Antunez-Quilobrán J, Baffico-Hernández A, Ávila-Sánchez C, Ortega-Farías S, Espinoza S, Gajardo J, Mora M, Fuentes S. Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance. Sensors. 2020; 20(12):3596. https://doi.org/10.3390/s20123596
Chicago/Turabian StyleCarrasco-Benavides, Marcos, Javiera Antunez-Quilobrán, Antonella Baffico-Hernández, Carlos Ávila-Sánchez, Samuel Ortega-Farías, Sergio Espinoza, John Gajardo, Marco Mora, and Sigfredo Fuentes. 2020. "Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance" Sensors 20, no. 12: 3596. https://doi.org/10.3390/s20123596
APA StyleCarrasco-Benavides, M., Antunez-Quilobrán, J., Baffico-Hernández, A., Ávila-Sánchez, C., Ortega-Farías, S., Espinoza, S., Gajardo, J., Mora, M., & Fuentes, S. (2020). Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance. Sensors, 20(12), 3596. https://doi.org/10.3390/s20123596