Estimation of physiological traits of tomato using thermography technique and NDVI sensor

Year 2019, Volume: 23 Issue: 1, 78 - 89, 25.03.2019

Gökhan Çamoğlu , Kürşad Demirel , Levent Genç

https://doi.org/10.29050/harranziraat.449224

Cited By: 5

Abstract

The aim of this study are to determine the
water stress level using the values obtained from the Normalized Difference
Vegetation Index (NDVI) sensor and the Crop Water Stress Index (CWSI) and also
relationships among some physiological traits (stomatal conductance, relative
leaf water content, leaf water potential, chlorophyll) of plant and CWSI/NDVI.
The study was conducted in Çanakkale province in 2017 investigated four
different irrigation treatments (100%, 75%, 50% and 25%). As a result of the
study, both remote sensing indices gave important responses to water stress. In
this case, it can be said that the water stress of the tomato can be determined
successfully by using both indices. The results indicated that physiological
measurements that are difficult to measure, time consuming and damaging to the
plant can be estimated with high accuracy by combined use of CWSI and NDVI
indices.

Keywords

Tomato, Water stress, Irrigation, CWSI, Spectral index

Termal kamera ve NDVI sensörü kullanılarak domatesin fizyolojik özelliklerinin tahminlenmesi

Abstract

Bu çalışmada; domates (Lycopersicum esculentum L. cv Full F1)
bitkisinde, bitki su stresi indeksi (CWSI) ve Normalize Edilmiş Vejetatif
Değişim İndeksi (NDVI) sensöründen elde edilen veriler kullanılarak su stresi
düzeyinin, ayrıca CWSI ve NDVI değerleri ile bitkinin bazı fizyolojik
özellikleri (stoma iletkenliği, yaprak su potansiyeli, yaprak oransal su
içeriği ve klorofil) arasındaki ilişkilerin belirlenmesi amaçlanmıştır.
Çanakkale ilinde 2017 yılında yürütülen çalışmada dört farklı sulama konusu
(%100, %75, %50 ve %25) ele alınmıştır. Çalışma sonucunda, uzaktan algılama
indekslerinin her ikisi de su stresi karşısında belirgin tepkiler vermiştir. Bu
durumda her iki indeks de kullanılarak domatesin su stresinin başarılı bir şekilde
belirlenebileceği söylenebilir. Buna ilaveten ölçümü zor, zaman alıcı ve
bitkiye zarar verebilen fizyolojik ölçümlerin CWSI ve NDVI değerlerinin her
ikisini de kullanarak yüksek doğrulukla tahmin edilebileceği sonucuna
varılmıştır.

Keywords

Domates, CWSI, Su stresi, Sulama, Spektral indeks

References

• Ackley, W.B., 1954. Water Contents and Water Deficits of Leaves of Bartlett Pear Trees on the Two Rootstocks P. Communis and P. Serotina. Proceedings of the American Society for Horticultural Science, 64: 181-185.
• Akçan, M., Çamoğlu, G., Demirel, K., 2016. Termografi Tekniğini Kullanarak Çimin Su Stresinin Belirlenmesi. 13. Ulusal Kültürteknik Kongresi, 12-15 Nisan. 346-354s. Antalya.
• 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.
• Camoglu, G., Genc, L., 2013. Determination of Water Stress Using Thermal and Spectral Indices from Green Bean Canopy. Fresenius Environmental Bulletin, 22(10a): 3078-3088.
• Camoglu, G., Kaya U., Akkuzu, E., Genc, L., Gurbuz, M., Pamuk Mengu, G., Kızıl, U. 2013. Prediction of Leaf Water Status Using Spectral Indices at Young Olive Trees. Fresenius Environmental Bulletin, 22(9a): 2713-2720.
• Camoglu, G., Demirel, K., Genc, L., 2018. Use of Infrared Thermography and Hyperspectral Data to Detect Effects of Water Stress on Pepper. Quantitative InfraRed Thermography Journal, 15(1): 81-94.
• Cohen, Y., Alchanatis, V., Meron, M., Saranga S., Tsipris, J. 2005. Estimation of Leaf Water Potential by Thermal Imagery and Spatial Analysis. Journal of Experimental Botany, 56: 1843-1852.
• Covey, R., 1999. Remote Sensing in Precision Agriculture: An Educational Primer, Iowa State University, Ames Remote, http://www.amesremote.com/papers.htm, Erişim tarihi: 14 Temmuz 2018.
• Datt, B., 1998. Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves. Remote Sensing of Environment, 66: 111-121.
• Demirel, K., Camoglu, G., Genc, L., Kizil, U., 2014. The Variation of Plant Stress Indicators and Some Traits Under Different Irrigation and Nitrogen Levels in the Rocket. Fresenius Environmental Bulletin, 23 (5):1238-1248.
• Jacquemoud, S., Ustin, S.I., 2001. Leaf optical properties: A state of the Art. Proc. 8th Int. Symp. “Phyisical Measurements and Signatures in Remote Sensings”, 8-12 Jan,2001, 223-232 pp. France.
• Jones, H.G., 1999. Use of Infrared Thermometry for Estimation of Stomatal Conductance as A Possible Aid to Irrigation Scheduling. Agricultural and Forest Meteorology, 95: 139-149.
• Jones, H.G., Stoll, M., Santos, T., de Sousa, C., Chaves, M.M., Grant, O., 2002. Use of Infrared Thermography for Monitoring Stomatal Closure in the Field: Application to Grapevine. Journal of Experimental Botany, 53: 2249-2260.
• Jones, C.L., Weckler, P.R., Maness, N.O., Stone, M.L., Jayasekara, R., 2004. Estimating Water Stress in Plants Using Hyperspectral Sensing, ASAE/CSAE Annual International Meeting, 1-4 August, Paper Number: 043065.
• Köksal, E.S., Üstün, H., Özcan, H., Güntürk, A., 2010. Estimating Water Stressed Dwarf Green Bean Pigment Concentration Through Hyperspectral Indices. Pakistan Journal of Botany, 42(3): 1895-1901.
• Mastrorilli, M., Campi, P., Palumbo, A.D., Modugno, F., 2010. Ground-Based Remote Sensing for Assessing Tomato Water-Status. Italian Journal of Agronomy, 5: 177-183.
• Monteith, J.L., Unsworth, M.L., 1990. Principles of Environmental Physics, 2nd ed. Edward Arnold, London, United Kingdom, 414.Morales, I., Urrestarazu, M., 2013. Thermography Study of Moderate Electrical Conductivity and Nutrient Solution Distribution System Effectson Grafted Tomato Soilless Culture. Hortscience, 48(12): 1508-1512.
• Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., Ostrovsky, V., Sprintsin, M., Cohen, S., 2007. Use of Thermal and Visible Imagery for Estimating Crop Water Status of Irrigated Grapevine. Journal of Experimental Botany, 58: 827-838.
• Nardella, E., Giuliani, M.M., Gatta, G., Tarantino, E., De Caro, A., 2008. Irrigation Scheduling in Processing Tomato Crop Cultivated in Southern Italy: the Role of Physiological Parameters. Italian Journal of Agronomy, 3(3): 685-686.
• Nicacias, M.M., 2009. Evaluating the Effect of Moisture Stress on Tomato Using Non-Destructive Remote Sensing Techniques. Master Thesis, School of Agricultural And Environmental Science, Faculty of Science and Agriculture, University of Limpopo.
• O’Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D., Howell, T.A., 2011. Using Radiation Thermography and Thermometry to Evaluate Crop Water Stress in Soybean and Cotton. Agricultural Water Management, 98: 1523-1535.
• Penuelas, J., Inoue, Y., 1999. Reflectance Indices Indicative of Changes in Water and Pigment Content of Peanut and Wheat Leaves. Photosynthetica, 36(3): 335-360.
• Penuelas, J., Inoue, Y. 2000. Reflectance Assessment of Canopy CO2 Uptake. International Journal of Remote Sensing, 21: 3353–3356.
• Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. In: Proceedings of the Third ERTS Symposium, December 1973 (Goddard Space Flight Center), Washington, DC: NASA, 309–317, NASA SP-351.
• Smart, R.E., Bingham, G.E., 1974. Rapid Estimates of Relative Water Content. Plant Physiology, 53: 258-260.
• Sönmez, N.K., Aslan, G.E., Kurunç, A., 2015. Farklı Tuz Stresi Altındaki Domates Bitkisinin Spektral Yansıma İlişkileri. Tarım Bilimleri Dergisi, 21: 585-595.
• Zia, S., Romano, G., Spreer, W., Sanchez, C., Cairns, J., Araus, J.L., Müller, J., 2013. Infrared Thermal Imaging as a Rapid Tool for Identifying Water-Stress Tolerant Maize Genotypes of Different Phenology. Journal of Agronomy and Crop Science, 199: 75-84.