Buğday’da Hiperspektral Veriler Kullanılarak Sarı Pas Hastalığının İzlenmesinde Spektral Oranlama Tekniğinin Kullanımı

Yıl 2024, Cilt: 14 Sayı: 1, 39 - 51, 01.03.2024

Metin Aydoğdu , Kadir Akan

https://doi.org/10.21597/jist.1300631

Öz

Buğdayda sarı pas hastalığı, küresel düzeyde yaşanabilen epidemiler nedeniyle gıda güvenliğini tehdit eden önemli fungal streslerden birisidir. Bu çalışma ile hastalığın (Etmen; Puccinia striiformis f. sp. tritici), buğdayda farklı fenolojik dönemlerde izlenmesi ve reaksiyon değişimlerinin ortaya konulabilmesi için spektral yansıma değerleri kullanılmıştır. Çalışma kapsamında hastalık inoküle edilmeyen ve hastalık inoküle edilen test materyallerinde yapılan değerlendirmeler sonucu elde edilen spektral yansıma değerleri oranlanmış, geliştirilen grafikler yorumlanarak stresin farklı bant aralıklarındaki gelişimi değerlendirilmiştir. 2018-2019 yetiştirme sezonunda yürütülen çalışmada bitki materyali olarak; Bayraktar 2000, Demir 2000, Eser ve Kenanbey ekmeklik çeşitleri ile Çeşit-1252, Eminbey, Kızıltan 91 ve Mirzabey 2000 makarnalık çeşitleri kullanılmıştır. Test materyalinin tümü Ekim ayı içerisinde, 33-35 cm sıra arası ve 1 m uzunluğundaki sıralara 3 tekerrürlü olarak elle ekilmiştir. Hastalığın yeni toplanmış sporları uçucu mineral yağ (Soltrol 170®) kullanılarak %0, %25, %50, %100 uygulama dozlarında test materyaline inoküle edilmiştir. Hastalığın reaksiyon değerlendirmeleri 25 Mayıs ile 06, 15, 23 Haziran tarihlerinde yapılmış olup enfeksiyon kat sayıları hesaplanmıştır. Çalışma sonucunda; hastalık inoküle edilmeyen ve hastalık inoküle edilen test materyallerinde değerlendirmelerle yansıma oranları dikkate alınarak farklı fenolojik dönemler için yansıma eğrileri oluşturulmuştur. Hastalık inoküle edilen genotiplerde spektral yansıma değerlerinin, aynı gelişme dönemi sürecinde görünür bölgede arttığı, yakın kızılötesi bölgede azaldığı belirlenmiştir. Hastalık reaksiyonunun değerlendirmesinde kullanılabilir en etkili hastalık dozu ekmeklik çeşitler için %50, makarnalık çeşitler için %25 dozu olarak değerlendirilmiştir. Çalışma ile; buğdayın farklı büyüme evrelerinde kanopinin spektral yansımalarındaki dinamik değişimler, hastalık reaksiyonuyla olan ilişkilerin sayısal olarak analiz edilmesinin mümkün olduğu değerlendirilmiştir. Farklı bant aralıklarında bu bölgelerdeki değişimlerin, yaprakların yaşlanma sürecine bağlı olarak mezofil dokulardaki pigmentlerin kapsamı ile ilişkili olduğunu düşündürmektedir.

Anahtar Kelimeler

Buğday, Çok bantlı veri, Sarı pas

Destekleyen Kurum

Tarla Bitkileri Merkez Araştırma Enstitüsü Müdürlüğü Yenimahalle / ANKARA

Teşekkür

Bu Araştırma 2018-2019 Yılları arası Tarla Bitkileri Merkez Araştırma Enstitüsü Patoloji deneme alanında Yürütülen “Kışlık buğdayda farklı demir ve çinko uygulamalarının sarı pas (Puccinia striiformis f. sp. tritici) hastalığı üzerine olan mevsimsel etkilerinin çok bantlı veriler kullanılarak belirlenmesi” isimli Yüksek Lisans tezi (YÖK Tez No: 671046/Tarih: 25.05.2021) verilerinin bir kısmından faydalanılarak hazırlanmıştır. Tez jürisinde bulunan sayın Prof. Dr. Hikmet GÜNAL ve sayın Dr. Öğr. Üyesi Nurullah ACİR’e katkılarından dolayı teşekkür ederiz. Çalışmaya katkılarından dolayı Dr. Nilüfer AKCİ’ye teşekkür ederiz

Use of Spectral Ratio Technique in Monitoring Yellow Rust Disease Using Hyperspectral Data in Wheat

Öz

Yellow rust disease in wheat is one of the major fungal pressures threatening food security as a result of global outbreaks. In this work, spectral reflection values were employed to monitor the disease (Caused by; Puccinia striiformis f. sp. tritici) and indicate reaction variations in wheat over distinct phenological phases. The spectrum reflection values obtained as a consequence of the assessments done in the genotypes with and without the disease were apportioned within the scope of the study, and the development of the stress in different band gaps was tracked by reading the graphs created. As research plant material in the study carried out in the 2018-2019 growing season; Bayraktar 2000, Demir 2000, Eser and Kenanbey bread varieties and Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000 durum varieties were used. All of the test material was sown by hand in 3 replications in rows 33-35 cm apart and 1 m long in October. Using essential mineral oil (Soltrol 170®), the newly collected disease spores were inoculated into the test material at doses of 0%, 25%, 50%, and 100%. Evaluations of disease’s reactions were made using the Modified Cobb scale between 25 May and 06, 15 and 23 June, and infection coefficients were derived using these results. As a result of the investigation, reflection curves for various phenological periods were constructed, accounting for reflection rates based on assessments of genotypes with and without illness. It was discovered that throughout the same developmental period, the spectral reflectance values of the disease-inoculated genotypes increased in the visible region and reduced in the near infrared area. The most effective disease dose to utilize in assessing the disease reaction was determined to be 50% for bread types and 25% for durum varieties. With effort, it was demonstrated that it was able to numerically analyses the correlations with the disease reaction through dynamic changes in the spectrum reflections of the canopy at various growth stages of wheat. Variations in these regions at various band ranges are assumed to be related to the extent of the pigments in mesophyll tissues as a result of the ageing process of leaves.

Anahtar Kelimeler

Wheat, Hyperspectral Data, Yellow rast

Kaynakça

• Anonim, (2019). Ankara Yenimahalle lokasyonu iklim verileri. T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı Meteoroloji Genel Müdürlüğü
• Akan, K., (2019). Sarı Pas (Puccinia striiformis f. sp. tritici) hastalığına dayanıklı makarnalık buğday hatlarının geliştirilmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 6: 661-670.
• Aktas, H., Karaman M., Tekdal, S., Kılıç, H. & Kendal E. (2012). Evaluating of yield losses caused by yellow rust pressure in some bread wheat genotypes. 13th International Cereal Rusts and Powdery Mildews Conference. Beijing, Chine. Abstract book: Volume I., 16 p.
• Braun, H.J. & Saari E.E. (1992). An assessment of the potential of Puccinia striiformis f. sp. tritici to cause yield losses in wheat on the Anatolian Plateau of Turkey. Vortr. Planzenzuchhtg.24,121-123.
• Campbell, J.B. & Wynne, R.H. (2011). Introduction to remote sensing. Guilford Press. New York.
• Cat, A., Tekin, M., Akan, K., Akar, T., & Catal, M. (2023). Virulence characterization of the wheat stripe rust pathogen, Puccinia striiformis f. sp. tritici, in Turkey from 2018 to 2020. Canadian Journal of Plant Pathology, 45(2), 158-167.
• Çat A, Tekin M, Çatal M, Akan K, & Akar T. (2017). Buğdayda sarı pas hastalığı ve dayanıklılık ıslahı çalışmaları. Mediterranean Agricultural Sciences, 30(2), 97-105.
• Chen X., 2005. Epidemiology and control of stripe rust (Puccinia striiformis f. sp. tritici) on wheat. Canadian Journal of Plant Pathology, 27(3), 314- 337.
• Devadas, R., Lamb, D.W., Backhouse, D. & Simpfendorfer, S. (2015). Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precision Agriculture 16, 477–491 https://doi.org/10.1007/s11119-015-9390-0
• Düşünceli, F., Çetin, L. ve Albustan, S. (1996). Occurrence and Impact of wheat stripe rust (Puccinia striiformis) in Turkey in 1994/95 crop season. Cereal Rusts and Powdery Mildews Bulletin.24, Supplement.309 p. Proc. of the 9th CR&PMC,2-6 September 1996, Lunteren, The Netherlands.
• Eversmeyer, M. G. & Kramer, C.L. (2000). Epidemiology of wheat leaf and stem rust in the central great plains of the USA, Annual Review of Phytopathology. 38, 491–513.
• Feng, W., Qi, S., Heng, Y., Zhou, Y., Wu, Y., Liu, W., He, L. & Li, X. (2017). Canopy vegetation indices from in situ hyperspectral data to assess plant water status of winter wheat under powdery mildew stress. Frontiers in Plant Science, 8,1219. https://doi.org/10.3389/fpls.2017.01219.
• Gitelson, A., Kaufman, Y.J., Stark, R. & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76-87.
• Hatfield, P.L. & Pinter Jr, P.J. (1993). Remote sensing for crop protection. Crop Protection, 12 (6), 403-413. https://doi.org/10.1016/0261-2194(93)90001-Y.
• Kolmer, J. A. (2005). Tracking wheat rust on a continental scale. Current Opinion in Plant Biology, 8(4), 441-449.
• Large, E.C. (1954). Growth stages in cereals illustration of the Feekes scale. Plant Pathology. 3 (4): 128–129. D https://doi.org/10.1111/j.1365-3059.1954.tb00716.x.
• Li, G.B., Zeng, S. M. & Li, Z.Q. (1989). Integrated management of wheat pests (pp. 185-186). Beijing: Press of Agriculture Science and Technology of Chine.
• Lillesand, T.M. & Kiefer, R.W. (1994). Remote Sensing and Image Interpretation, John Wiley & Sons, Inc., New York, USA.
• Lu, J., Ehsani, R., Shi, Y., de Castro, A.I. & Wang, S. (2018). Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Scientific Reports. (Nature), 8, 2793.
• Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plumer, L., Steiner, U. ve Oerke, E.C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30.
• Merzlyak, M.N. & Chivkunova, O.B. (2001), Optical properties and nondestructive estimation of anthocyanin content in plant leaves, Photochemistry and Photobiology, 74(1), 38-45.
• Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A. & Ramon, H. (2004). Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 44 (3),173-188.
• Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A. & Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Korhonen maps. Real-Time Imaging 11(2), 75-83.
• Murray, G., Wellings, C., Simpfendorfer, S. & Cole, C. 2005. Stripe rust: Understanding the disease in wheat, New South Wales Department of Primary Industries. Retrieved from http://www.ricecrc.org/reader/wintercereals/stripe-rust in heat.pdf?MIvalObj=25431&doctype=document&MItypeObj=application/pd f&name=/stripe-rust-in-wheat.pdf.
• Myers, V.I. (1983). Remote sensing applications in agriculture (in Manual of Remote Sensing) American Soc. Photogramm, and Rem. Sens., Falls Church, Va 2111-228
• Naidu, R.A., Perry, E.M., Pierce, F.J. & Mekuria, T., (2009). The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture 66, 38–45.
• Nicolas, H., (2004). Using remote sensing to determine of the date of a fungicide application on winter wheat, Crop Protection, 23(9), 853-863.
• Nilsson, H.E., (1995a.). Remote Sensing and Image Analysis in Plant Pathology. Annual Review of Phytopathology, 33:489-528. https://doi.org/10.1146/annurev.py.33.090195.002421.
• Nilsson, H.E. (1995b). Remote sensing and image analysis in plant pathology, Canadian Journal of Plant Pathology, 17:2, 154-166, https://doi.org/10.1080/07060669509500707
• Penuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221-230.
• Peterson, R.F. Campbell, A.B. & Hannah, A.E. (1948). A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Canadian Journal of Research. 26 (Section C),496-500.
• Qin, Z. & Zhang, M. (2005). Detection of rice sheath blight for in-season disease management using multispectral remote sensing, International Journal of Applied Earth Observation and Geoinformation, 7(2), 115-128.
• Roelfs, A. P. (1978). Estimated losses caused by rust in small grain cereals in the United States, 1918-76 (No. 1363). US Department of Agriculture, Agricultural Research Service.
• Roelfs, A.P., Singh, R.P., & Saari. E.E. (1992). Rust Diseases of Wheat: Concepts and methods of disease management. Mexico, D.F.: CIMMYT. 81 pages.
• Samborski, D.J. (1985).'Wheat leaf rust, in the cereal rusts, Vol. 2, Diseases, distribution, epidemiology, and control, A.P. Roelfs, and Bushnell, W.R. (ed), Academic Press, Orlando, FL, USA, 39-55 pp.
• Saari, E.E. & Prescott, J.M. (1985). World distribution in relation to economic losses., in the cereal rusts, Vol. 2, Diseases, distribution, epidemiology, and control, A.P. Roelfs and W.R. Bushnell (eds), Academic Press, Orlando, FL, USA, 259-298 pp.
• Tekin, M., Cat, A., Akan, K., Catal, M., & Akar, T. (2021). A new virulent race of wheat stripe rust pathogen (Puccinia striiformis f. sp. tritici) on the resistance gene Yr5 in Turkey. Plant Disease, 105(10), 3292.
• Tekin, M., Cat, A., Akan, K., Demir, H., & Akar, T. (2022). Evaluation of resistance of Turkish bread wheat (Triticum aestivum) varieties to recently emerged Puccinia striiformis f. sp. tritici races. Physiological and Molecular Plant Pathology, 122, 101928.
• Watkins, J. E. (2006). Leaf, stem & stripe rust diseases of wheat. Neb Guide: University of Nebraska Lincoln. Retrieved March 23, 2006 from http://elkhorn.unl.edu/epublic/pages/publicationD.jsp? publicationId=310#top.
• Yuan, L., Zhang, J.C., Wang, K., Loraamm, R.W., Huang, W.J., Wang, J.H., & Zhao, J.L. (2013). Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat. Precision Agriculture, 14, 495-511.
• Zhang, M., Qin, Z., & Ustin, S.L. (2003). Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation 4(4):295-310. https://doi.org/10.1016/S0303-2434(03)00008-4.
• Zhang, J., Pu, R., Huang, W., Yuan, L., Luo, J. & Wang, J. (2012). Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crops Research, 134, 165–174