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USE OF INFRARED THERMOGRAPHY ON CANOPIES AS INDICATOR OF WATER STRESS IN 'ARBEQUINA' OLIVE ORCHARDS

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USE OF INFRARED THERMOGRAPHY ON CANOPIES AS INDICATOR OF WATER STRESS IN 'ARBEQUINA' OLIVE ORCHARDS
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  399 Use of Infrared Thermography on Canopies as Indicator of Water Stress in ‘Arbequina’ Olive Orchards C. Poblete-Echeverría a , S. Ortega-Farías and M. Zuñiga Centro de Investigación y Transferencia en Riego y Agroclimatología (CITRA) Universidad de Talca Chile G.A. Lobos, S. Romero and F. Estrada Centro de Mejoramiento Genético y Fenómica Vegetal (CMGFV) Universidad de Talca Chile S. Fuentes The University of Melbourne Melbourne School of Land and Environment Victoria 3010 Australia Keywords: regulated deficit irrigation, pressure chamber, Olea europaea ,   thermography, crop water stress index (CWSI) Abstract Irrigation scheduling is critical for olive orchards, since it affects both fruit yield and olives composition. Regulated deficit irrigation (RDI) strategies have been applied with positive results in the past. However, to successfully regulate stress levels, it is necessary to have accurate measurements of plant water status, which is usually done using a pressure chamber. Canopy temperature (T c ) is another potential accurate indicator of water stress. Therefore, the objective of this study was to evaluate three methods to obtain T c  values from infrared thermal images to calculate the crop water stress index (CWSI). Furthermore, the relation between CWSI and midday stem water potential (MSWP) was also studied. The methods used to obtain T c  were: i) T c1  obtained from a region of interest within the image; ii) T c2  obtained from whole image; iii) T c3  obtained from a filtered image using an interactive filtering process to exclude non-leaf material (low and high temperature values). The infrared thermal images were obtained using an infrared camera (Model i40, FLIR Instruments) in parallel with MSWP measurements from trees under different RDI strategies in a drip irrigated olive orchard ( Olea europaea  L. ‘Arbequina’) located in Pencahue valley, Maule Region, Chile (35°23’L.S; 71°44’L.W; 96 m a.s.l.) during the 2011-2012 season. Results obtained in this study showed that CSWI 3  calculated using T c3  had a better correlation with MSWP compared to the two other methods studied. The interactive filter process to obtain T c  values could be used in olive orchards as a fast and cheap indicator of water stress. Further studies are required to automate the analysis process. INTRODUCTION Irrigation scheduling is critical for olive orchards, since it affects both fruit yield and olive composition. Regulated deficit irrigation (RDI) strategies have been successfully applied to optimize water application, yield and olive oil content during the last decade. However, the practical application of RDI requires an accurate knowledge of  plant water status, which is usually performed using a pressure chamber. Useful information on canopy temperature and water relations can be derived from infrared thermography (IRT). IRT techniques have been used in agriculture as a non-invasive and a  cpoblete@utalca.cl Proc. VII th  IS on Olive Growing Eds.: F. Vita Serman et al. Acta Hort. 1057, ISHS 2014  400 versatile imaging tool to investigate biotic and abiotic stresses (O’Shaughnessy et al., 2011). In this regard, Jones et al. (2002) studied the effect of abiotic stresses with thermal imagery and they concluded that IRT allows the semi-automated analysis of large areas of canopy with much more effective replication compared to the one achieved using  porometry or single point measurements. Furthermore, Leinonen and Jones (2004) classified thermal images to identify leaf area, sunlit, and shaded parts of the canopy. Their methods improved estimates of temperature distribution across a canopy by separating out mixed pixels and reducing the effects of thermal contribution from  background, and angle of view (Luquet et al., 2003). A representative canopy temperature (Tc) could be used as a good indicator of  plant water stress, since when plants are undergoing water stress they increase their temperature (Jones et al., 2002; Wang et al., 2010). However, it is critical to identify only the leaf material from the thermal image to avoid over or underestimations of CWSI due to non-leaf material inclusion in the analysis. Therefore, the objective of this study was to evaluate three methods to obtain Tc values from infrared thermal images: i) T c1  obtained from a region of interest (ROI) of the image; ii) T c2  obtained from whole image; iii) T c3  obtained from a filtered image using an interactive filtering process to exclude non-leaf material (low and high temperature values). Also, T c1 , T c2  and T c3  from infrared thermography were used to study the relationship between crop water stress index (CWSI) and midday stem water potential (MSWP). MATERIALS AND METHODS The infrared thermal images were obtained using an infrared camera (Model i40, FLIR Instruments) in parallel with measurements of midday stem water potential (MSWP) on olive trees under different RDI strategies. The IRT technique was tested on a drip-irrigated olive orchard ( Olea europaea  L. ‘Arbequina’) located in the Pencahue valley, Maule Region, Chile (35°23’L.S; 71°44’L.W; 96 m a.s.l.) during the 2011-2012 season (2 dates: 13-01-12 and 27-01-12). T c  values were calculated from infrared thermal images using three methods: i) T c1  obtained from a ROI from the image (central point of the image) (Fig. 1a); ii) T c2  obtained from whole image (average) (Fig. 1b); iii) T c3  obtained from a filtered image using an interactive filtering process to exclude non-leaf material (Fig. 1c). This filtering process was carried out using a code written in MATLAB ®  2009a (The Mathworks Inc., Natick, MA, USA). The use of the semi-automated code requires that all thermal images (Joint Photographic Experts Group JPEG) are saved in a Microsoft Excel file, in which each image is stored as a separate worksheet (Fuentes et al., 2012). To change the file format, thermal images were loaded using the FLIR QuickReport software (FLIR Systems, Portland USA) and exported to Excel file. Thermal images were imported and stored in matrix variables automatically, which can be treated in MATLAB as 8-bit indexed images. The crop water stress index (CWSI) was calculated from T c  and reference leaf surfaces corresponding to fully transpiring (T wet ) and non-transpiring leaves (T dry ) (Jones et al., 2002; Möller et al., 2007): wetdrywetc TTTTCWSI   (1) where, T c  is the canopy temperature (°C), T wet  is the average temperature of a wet reference (°C) (fully transpiring leaf) and T dry  is the dry temperature (°C) (Jones et al., 2002; Fuentes et al., 2012). In this study, empirical reference temperatures (T wet  and T dry ) were computed from infrared thermal images (minimum and maximum values, respectively). Midday stem water potential (MSWP) was measured using a pressure chamber (PMS600, PMS Instruments Company, Corvallis, Oregon, USA). The measurements with  pressure chamber were made immediately after cutting shoots, which were selected from  401 the middle zone of the canopy and previously bagged in plastic bags coated with aluminum foil for at least 2 h before measurements (Choné et al., 2001). The comparison  between MSWP versus CWSI 1 , CWSI 2  and CWSI 3  was carried out using a linear regression analysis. Then estimated MSWP values (MSWP 1 , MSWP 2  and MSWP 3 ) were evaluated using the root mean square error (RMSE) and mean absolute error (MAE). RESULTS AND DISCUSSION Canopy temperature data (T c1 , T c2  and T c3 ) obtained from infrared thermography ranged from 28.2 to 44.2°C for T c1 , 30.3 to 37.7°C for T c2  and 30.4 to 39.4°C for T c3 . The CWSI ranged from 0 (no stress) to nearly 1 (stressed), at which point T c  was closest to T dry . The average values of CWSI 1 , CWSI 2  and CWSI 3  obtained in this study were 0.34 (±0.12), 0.30 (±0.13) and 0.34 (±0.16), respectively. Linear regressions of CWSI 1 , CWSI 2  and CWSI 3  versus MSWP showed an inverse relationship between MSWP and CWSI (Fig. 2) for the three methods proposed to obtain T c . Furthermore, the relationship between MSWP and CWSI1 was not statistically significant at the 95.0% or higher confidence level (Table 1). A weak correlation was found between CWSI 1  and MSWP, which can be related to higher variation of the temperature values obtained from the central point of the infrared images, as shown in Figure 2. The statistical analysis showed that there was a significant negative linear correlation between MSWP versus the CWSI 2  and MSWP versus CWSI 3 , with r  2  values of 0.44 and 0.73, respectively. The linear relationships of CWSI 3  versus MSWP were similar to those reported by Ben-Asher et al. (1992) in cotton using the empirical CWSI developed by Idso et al. (1981). Furthermore, by using measurements of water potential, Cohen et al. (2005) and Möeller et al. (2007) described similar relationships for cotton and grapevine plants, respectively. CWSI3 calculated using T c3  showed a better correlation with MSWP (r  2 =0.73). Finally, the comparison between MSWP measured versus MSWP 3  calculated using CWSI 3  presented the lower error with RMSE and MAE equal to 0.78 and 0.64 MPa, respectively (Table 1). Fuentes et al. (2012) using a similar filtering process in grapevines, found good correlations between leaf conductance and CWSI calculated by infrared thermal image analysis. The results obtained in this study confirm that the interactive filter process allowed obtaining adequate canopy temperatures values, which could be used in olive orchards as a fast and cheap indicator of water stress compared to manual techniques such as pressure chamber or porometry. CONCLUSIONS  It has been demonstrated in this study that the exclusion of non-leaf material from the IRT analysis is critical to obtain representative and meaningful T c values to calculate CWSI. The IRT technique presents several advantages over other commonly used methods, such as pressure chamber and porometry: i) it represents the whole-canopy water status (as the MSWP), ii) it does not require heavy instrumentation or cylinders, iii) the image acquisition and analysis could be automated for a more spatial representation of the orchard. More studies needs to be done towards the automation of image acquisition and analysis for different crops and orchards. ACKNOWLEDGEMENTS The research leading to this report was supported by the scientific projects FONDECYT POSTDOCTORAL (Nº 3100128), CONICYT (Nº 79090035), FONDEF (D10I1157) and the research program “Adaptation of Agriculture to Climate Change (A2C2)” of the Universidad de Talca. Literature Cited Ben-Asher, J., Phene, C.J. and Kinarti, A. 1992. Canopy temperature to assess daily evapotranspiration and management of high frequency drip irrigation systems. Agri. Water Manag. 22(4):379-390.  402 Choné, X.C., Van Leeuwen, C., Dubourdieu, D. and Gaudillère, J.P. 2001. Stem water 575 potential is a sensitive indicator of grape water status. Ann. Bot. 87:477-483. Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y. and Tsipris, J. 2005. Estimation of leaf water potential by thermal imagery and spatial analysis. J. Exp. Bot. 56:1843-1852. Fuentes, S., Bei, R., Pech, J. and Tyerman, S. 2012. Computational water stress indices obtained from thermal image analysis of grapevine canopies. Irrig. Sci. 30:523-536. Idso, S.B., Reginato, R.J., Reicosky, D.C. and Hatfield, J.L. 1981. Determining soil induced plant water potential depressions in alfalfa by means of infrared thermometry. Agronomy J. 73:826-830. Jones, H.G., Stoll, M., Santos, T., de Sousa, C., Chaves, M.M. and Grant, O.M. 2002. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J. of Exp. Bot. 53(378):2249-2260. Leinonen, I. and Jones, H.G. 2004. Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. J. Exp. Bot. 55(401):1423-1431. Luquet, L., Begue, A., Vidal, A., Clouvel, P., Dauzat, J., Olioso, A., Gu, X.F. and Tao, Y. 2003. Using multidirectional thermography to characterize water status of cotton. Rem. Sens. Environ. 84:411-421. Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., Ostrovsky, V., Sprintsin, M. and Cohen, S. 2007. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J. of Exp. Bot. 58(4):827-838. O’Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D. and Howell, T.A. 2011. Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agri. Water Manag. 98:1523-1535. Wang, X., Yang, W., Wheaton, A., Cooley, N. and Moran, B. 2010. Efficient registration of optical and IR images for automatic plant water stress assessment. Comp. and Elec. in Agri. 73:74-83. Tables Table 1. Statistical analysis of linear regression between midday stem water potential (MSWP) versus crop water stress index estimated using temperature obtained from a selected section of the image (CWSI 1 ); whole image (CMSI 2 ) and filtered image (CWSI 3 ). Variable r  2  Slope   Intercept p-value RMSE MAE CWSI 1  0.09 -4.29 -4.29 0.0877 1.42 1.19 CWSI 2  0.44 -16.08 0.14 0.0001 1.12 0.85 CWSI 3  0.73 -15.89 1.06 0.0000 0.78 0.64 r    2  is the determination coefficient, RMSE is the root mean square error (MPa); MAE is the mean absolute error (MPa).  403 Figures Fig. 1. Example of the three selection methods: a) temperature obtained from a selected section of the image (central point of the image); b) temperature obtained from whole image; c) temperature obtained from a filtered image using an interactive filtering process to exclude non-leaf material. Black color in the filtered image corresponds to non-leaf material exclusion. Fig. 2. Comparison between midday stem water potential (MSWP) and the crop water stress index, (CWSI) calculated using temperatures extracted from thermometric images. Black circles CWSI 1 calculated using T c1  obtained from a selected section of the image; Gray circles CSWI 2  calculated using T c2  obtained from whole image and white circles CWSI 3  calculated using T c3  obtained from a filtered image using an interactive filtering process to exclude non-leaf material.   -7.0-6.0-5.0-4.0-3.0-2.0-1.00.00.0 0.1 0.2 0.3 0.4 0.5 0.6    S   t  e  m   w  a   t  e  r  p  o   t  e  n   t   i  a   l   (   M   P  a   ) CWSI (dimensionless) a) b) c)
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