Investigation of Salt Stress in Rosemary (Rosmarinus officinalis L.) with the Remote Sensing Technique

Abstract

Abstract: In the present work, the effect of different salt concentrations on growth and quality of rosemary (Rosmarinus officinalis L.) was investigated using ground-based remote sensing techniques under greenhouse conditions in 2018. The experiment was carried out in a randomized complete block design with three replications and lasted 8 weeks in total. Spectroradiometer measurements were performed on the leaves of rosemary plants to monitor changes in spectral signatures due to salt stress. Spectrophotometer and chlorophyll meter measurements were also taken from the leaves of the plants to investigate the reactions to salt stress at the end of 4th and 8th weeks simultaneously with all other measurements. According to the obtained data, there was a significant difference in the chlorophyll, brightness and colour values of the leaves in response to salt stress, and a difference was observed in the reflective values of the plants in the spectral measurements taken at 4th and 8th weeks. The region with the biggest difference between reflectance values was near-infrared among different salt concentrations applied plants. In the 4th week, the most vivid color (intense green) was obtained in the S3 and S4 applications, (19.80 and 19.40, respectively). However, as the stress application time and the applied salt rate increased, small changes in plant color occurred. Besides, it was concluded that salt treatment increased the NDVI (Normalized Difference Vegetation Index) values of the plants.

Keywords

• Salt stress
• spectral measurement
• rosemary
• chlorophyll measurement

Investigation of Salt Stress in Rosemary (Rosmarinus officinalis L.) with the Remote Sensing Technique

Abstract

In the present work, the effect of different salt concentrations on growth and quality of rosemary (Rosmarinus officinalis L.) was investigated using ground-based remote sensing techniques under greenhouse conditions in 2018. The experiment was carried out in a randomized complete block design with three replications and lasted 8 weeks in total. Spectroradiometer measurements were performed on the leaves of rosemary plants to monitor changes in spectral signatures due to salt stress. Spectrophotometer and chlorophyll meter measurements were also taken from the leaves of the plants to investigate the reactions to salt stress at the end of 4th and 8th weeks simultaneously with all other measurements. According to the obtained data, there was a significant difference in the chlorophyll, brightness and colour values of the leaves in response to salt stress, and a difference was observed in the reflective values of the plants in the spectral measurements taken at 4th and 8th weeks. The region with the biggest difference between reflectance values was near-infrared among different salt concentrations applied plants. In the 4th week, the most vivid color (intense green) was obtained in the S3 and S4 applications, (19.80 and 19.40, respectively). However, as the stress application time and the applied salt rate increased, small changes in plant color occurred. Besides, it was concluded that salt treatment increased the NDVI (Normalized Difference Vegetation Index) values of the plants.

Keywords

• Salt stress
• spectral measurement
• rosemary
• chlorophyll measurement

Supporting Institution

Sivas Cumhuriyet Üniversitesi

References

1. Açıkgöz, A., Açıkgöz, N., 2001. Common mistakes in the statistical analyzes of agricultural experiments, I. Single factorials. Anadolu, J. of AARI, 11(1): 135-147. (In Turkish).
2. Akram, F., Mahmood, F., Saifullah, M., Zafar, M., Saman, S. Yasmeen, S., Karamat, A., Tanveer, M.U., 2019. Identification of potential sites for rice plant growth using multi criteria decision (MCE) technique through remote sensing and GIS. International Journal of Agriculture and Sustainable Development, 1(4): 104-114.
3. Ali, R.R., Abd El Kader, A.A., Essa, E.F., AbdelRahman, M.A.E., 2019. Application of remote sensing to determine spatial changes in soil properties and wheat productivity under salinity stress. Plant Archives, 19(1): 616-621.
4. Allbed, A., Kumar, L., 2013. Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in Remote Sensing, 2(4): 373-385.
5. Amira, M.S., Qados A., 2011. Effect of salt stress on plant growth and metabolism of bean plant Vicia faba (L.). King Saud University Journal of the Saudi Society of Agricultural Sciences, 10: 7-15.
6. Argyrokastritis, I.G., Papastylianou, P.T., Alexandris, S., 2015. Leaf water potential and crop water stress index variation for full and deficit irrigation cotton in Mediterranean conditions. Agriculture and Agricultural Science Procedia, 4: 463-470.
7. Bayrak, A., 2006. Gıda aromaları. Gıda Teknolojisi Dergisi, 32: 268-273.
8. Bernstein, N., Lauchli, A., Silk, W.K., 1993. Kinematics and dynamics of sorghum (Sorghum bicolor L.) leaf development at various Na/Ca salinities (I. Elongation growth). Plant Physiology, 103(3): 1107-1114. Birdal, A.C, Avdan, U., Türk, T., 2017. Estimating tree heights with images from an unmanned aerial vehicle. Geomatics, Natural Hazards and Risk, 8(2): 1144-1156.

9. Caturegli, L., Grossi, N., Saltari, M., Gaetani, M., Magni, S., Nikolopoulou, A.E., Bonari, E., Volterrani, M., 2015. Spectral reflectance of tall fescue (Festuca arundinacea Schreb.) under different irrigation and nitrogen conditions. Agriculture and Agricultural Science Procedia, 4: 59-67.
10. Chemura, A., Mutanga, O., Dube, T., 2017. Remote sensing leaf water stress in coffee (Coffea arabica) using secondary effects of water absorption and random forest. Physics and Chemistry of the Earth, 30(100): 1-8.
11. Cordell, S., Questad, E.J., Asner, G.P., Kinney, K.M., Thaxton, J.M., Uowolo, A., Brooks, S., Chynoweth, M.W., 2017. Remote sensing for restoration planning: how the big picture can inform stakeholders. Restoration Ecology, 25(S2): 147-154.
12. Çamoğlu, G., Genç, L., 2013. Use of thermal imaging and spectral data to detect water stress in green bean. COMU Journal of Agriculture Faculty, 1(1): 15-27. (In Turkish).
13. Çulha, Ş., Çakırlar, H., 2011. The effect of salinity on plants and salt tolerance mechanisms. Afyon Kocatepe University Journal of Sciences, 11(2): 11-34. (In Turkish).
14. Elmetwalli, A.M.H., Tyler, A.N., Hunter, P.D., Salt, C.A., 2012. Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements. Remote Sensing Letters, 3(5): 363-372.
15. Esti, M., Cinquanta, L., Sinesio, F., Moneta, E., Di Matteo, M., 2002. Physicochemical and sensory fruit characteristics of two sweet cherry cultivars after cool storage. Food Chemistry, 76(4): 399-405.
16. Freed, R.D., Eisensmith, S.P., Everson, E.H., Weber M., Paul, E., Isleib, D., 1988. MSTAT-C, A Microcomputer Program for the Design, Management and Analysis of Agronomic Research Experiments. Michigan State University Institute of International Agriculture, East Lansing, USA.
17. Gürsoy, Ö., Atun, R., 2019. Using remote sensing in detecting sugar beet fields treated with different doses of phosphorus. Fresenius Environmental Bulletin, 28(2A): 1247-1253.
18. Gürsoy, Ö., Canbaz, O., Gökçe, A., Atun, R., 2017. Spectral classification in lithological mapping: A case study of matched filtering. Cumhuriyet Science Journal, 38(4): 731-737.
19. Hatchell, D.C., 1999. Analytical Spectral Devices, Inc. (ASD) Technical Guide. Boulder, USA.
20. He, L., Zhang, H.Y., Zhang, Y.S., Song, X., Feng, W., Kang, G.Z., Wang, C.Y., Guo, T.C., 2016. Estimation canopy leaf nitrogen concentration in winter wheat based on multiangular hyperspectral remote sensing. European Journal of Agronomy, 73: 170-185. Hernandez, H.I., Pastor, I.M., Pedreno, J.N., Gomez, I., 2014. Spectral indices for the detection of salinity effects in melon plants. Scientia Agricola, 71(4): 324-330.
21. Hernandez, M., Rodriguez, E., Diaz, C., 2007. Free hydroxycinnamic acids, lycopene, and color parameters in tomato cultivars. Journal of Agricultural Food Chemistry, 55(21): 8604-8615.
22. Jones, C.L., Weckler, P.R., Maness, N.O., Jayasekara, R., Stone, M.R., Chrz, D., 2007. Remote sensing to estimate chlorophyll concentration in spinach using multi‐spectral plant reflectance. American Society of Agricultural and Biological Engineers, 50(6): 2267-2273.
23. Kalantari, E., Hassanli, A.M., 2012. Arid and semi-arid lands management assessment using biological recovery methods and the use of unconventional water. The First National Conference on Strategies for Achieving Sustainable Development in Agriculture, Natural Resources and the Environment, 10-11 March, Tehran, Iran.
24. Kalantari, E., Hassanli, A.M., Ghanbarian, G.A., Ghaemi, A.A., Mousavi, S.R., 2018. Local desalination treatment plant wastewater reuse and evaluation potential absorption of salts by the halophyte plants. Eurasian Journal of Soil Science, 7(1): 43-50.
25. Katsoulas, N., Elvanidi, A., Ferentinos, K.P., Kacira, M., Bartzanas, T., Kittas, C., 2016. Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review. Biosystems Engineering, 151: 374-398.
26. Kırpık, M., 2005. Researches on the yield and quality of rosemary (Rosmarinus officinalis L.) cultivars grown in dryland and highland conditions of Çukurova region. PhD thesis, University of Çukurova, Institute of Natural and Applied Sciences, Adana. (In Turkish).
27. Krezhova, D., Kirova, E., 2011. Hyperspectral remote sensing of the impact of environmental stress on nitrogen fixing soybean plants (Glycine max L.). Proceedings of 5th International Conference on Recent Advances in Space Technologies, 9-11 June, Turkey, pp. 172-177.
28. Larcher, W., 1995. Physiological Plant Ecology: Ecophysiology and Stress Physiology of Functional Groups. Springer-Verlag, New York.
29. Lemos, M.F., Pacheco, H.P., Endringer, D.C., Scherer, R., 2015. Seasonality modifies rosemary’s composition and biological activity. Industrial Crops and Products, 70: 41-47.
30. Lugassi, R., Goldshleger N., Chudnovsky A., 2017. Studying vegetation salinity: from the field view to a satellite-based perspective. Remote Sensing, 122(9): 1-16.
31. Mac Arthur, A.A., Maclellan, C., Malthus, T.C., 2007. Determining the FOV and directional response of field spectroradiometers. 5th EARSeL Workshop on Imaging Spectroscopy, Proceedings, April 23-25, Bruges, s. 1-8. McGuire, R.G., 1992. Reporting of objective color measurements. American Society for Horticultural Science, 27(12): 1254-1255.
32. Meggio, F., Zarco-Tejada, P.J., Nunez, L.C., Sepulcre- Canto, G., Gonzalez, M.R., Martin, P., 2010. Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiology remote sensing indices. Remote Sensing of Environment, 114(9): 1968-1986.
33. Olmedo, R.H., Nepote, V., Grosso, N.R., 2013. Preservation of sensory and chemical properties in flavoured cheese prepared with cream cheese base using oregano and rosemary essential oils. Lwt-Food Science Techology, 53(2): 409-417.
34. Orak, A., Ateş E., 2005. Resistance to salinity stress and available water levels at the seedling stage of the common vetch (Vicia sativa L.). Plant Soil and Environment, 51: 51-56.
35. Özyiğit, Y., 2017. An estimation of some plant traits with the remote sensing method in the narbon bean (Vicia narbonensis L.). Fresenius Environmental Bulletin, 26(10): 6043-6048.
36. Özyiğit, Y., Bilgen, M., 2013. Use of spectral reflectance values for determining nitrogen, phosphorus and potassium contents of rangeland plants. Journal of Agricultural Science and Technology, 15(7): 1537-1545.
37. Parry, C., Blonquis, J.M., Bugbee, B., 2014. In situ measurement of lead chlorophyll concentration: analysis of the optical/absolute relationship. Plant Cell and Environment, 37(11): 2508-2520.
38. Pinter, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S.T., Upchurch, D.R., 2003. Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69(6): 647-664.
39. Qurie, M., Daghra, S., Khamis, M., Kanan, A., Barghouthi, Z., Alimari, A., Nussiebah S., Karaman, R., 2019. Rosemary (Rosmarinus officinalis) plants irrigation with secondary treated effluents using epuvalisation technology. Net Journal of Agricultural Science, 7(2): 69-77.
40. Ramoelo, A., Dzikiti, S., Deventer, H.V., Maherry, A., Cho, M.A., Gush, M., 2015. Potential to monitor plant stress using remote sensing tools. Journal of Arid Environments, 113: 134-144.
41. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the great plains with ERTS. Third ERTS-1 Goddard Space Flight Center 3d ERTS-1, Paper A20, 01 January, Washington DC, pp. 309-317.
42. Saranwong, S., Sornsrivichai, J., Kawano, S., 2004. Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biology and Technology, 31(2): 137-145.
43. Simon, J.E., Chadwick A.F., Craker, L.E., 1984. Herbs: An Indexed Bibliography. 1971-1980 the Scientific Literature on Selected Herbs, and Aromatic and Medicinal Plants of the Temperate Zone. Archon Books, Hamden.
44. Sönmez, N.K., Aslan, G.E., Kurunç, A., 2015. Relationship spectral reflectance under different salt stress conditions of tomato. Journal of Agricultural Sciences, 21(4): 585-595. (In Turkish).
45. Sytar, O., Brestic, M., Zivcak, M., Olsovska, K., Kovar, M., Shao, H., He, X., 2017. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. Science of the Total Environment, 578: 90-99.
46. Tester, M., Davenport, R., 2003. Na tolerance and Na transport in Higher plants. Annals of Botany, 91(5): 503-527.
47. Tilling, A.K., O’leary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D., Belford, R., 2007. Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 1(3): 77-85.
48. Tiryaki, İ., 2018. Adaptation mechanisms of some field plants against to salt stress. Kahramanmaraş Sütçü İmam University Journal of Agriculture and Nature, 21(5): 800-808. (In Turkish).
49. Tounekti, T., Vadel, A.M., Onate, M., Khemira, H., Munné-Bosch, S., 2011. Salt-induced oxidative stress in rosemary plants: Damage or protection. Environmental and Experimental Botany, 71: 298-305.
50. Trigueros, C.R., Nortes, P.A., Alarcon, J.J., Hunink, J.E., Parra, M., Contreras, S., Droogers, P., Nicolas, E., 2017. Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing. Agricultural Water Management, 183: 60-69.
51. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150.
52. Yol, E., Uzun, B., 2011. Inheritance of number of capsules per leaf axil and hairiness on stem, leaf and capsule of sesame (Sesamum indicum L.). Agricultural Journal of Crop Science, 5(1): 78-81.
53. Yousef, S., Summy, K.R., Little, C.R., 2011. Detection of salt toxicity and nitrogen defıciency in Cucumis sativus L. using spectroradiometry and color infrared imagery. Journal of Plant Nutrition, 34: 1236-1244.
54. Yousfi, S., Kellas, N., Saidi, L., Benlakehal, Z., Chaou, L., Siad, D., Herda, F., Karrou, M., Vergara, O., Gracia, A., Araus, J.L., Serret, M.D., 2016. Comparative performance of remote sensing methods in assessing wheat performance under Mediterranean conditions. Agricultural Water Management, 164(1): 137-147.
55. Wang, L., Zhou, X., Zhu, X., Guo, W., 2017. Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data. Computers and Electronics in Agriculture, 140: 327-337.
56. Zhang, K., Ge, X., Liu, X., Zhang, Z., Liang, Y., Tian, Y., Cao, Q., Cao, W., Zhu, Y., Liu, X., 2017. Evaluation of the chlorophyll meter and GreenSeeker for the assessment of rice nitrogen status. Advances in Animal Biosciences: Precision Agriculture, 8(2): 1-5.