Bazı Ekmeklik ve Makarnalık Buğday Çeşitlerinde Sarı Pas Hastalığının Fenolojik Dönemlere göre Hastalık-Verim İlişkisinin Çok Bantlı (Hiperspektral) Veriler Kullanılarak Araştırılması

Year 2023, Volume: 37 Issue: 2, 401 - 423, 08.12.2023

Metin Aydoğdu , Kadir Akan

https://doi.org/10.20479/bursauludagziraat.1245805

Abstract

Bu çalışma, ekmeklik (Bayraktar 2000, Demir 2000, Eser ve Kenanbey) ve makarnalık (Çeşit-1252,
Eminbey, Kızıltan 91 ve Mirzabey 2000) buğday çeşitlerini kontrollü epidemi koşullarında farklı spor dozlarına (%0, %25, %50 ve %100) tabi tutarak farklı fenolojik dönemlerde sarı pas şiddetini değerlendirmeyi amaçlanmıştır. Araştırma 2018-2019 yetiştirme sezonunda Yenimahalle, Ankara, Türkiye'de yürütülmüştür. Çalışmada, kardeşlenmeden sapa kalkmaya kadar olan dönemde farklı spor dozu uygulamalarında el spektroradyometresi kullanılarak elde edilen yansıma değerleri ile test materyallerinin farklı fenolojik gelişim dönemlerinde sarı pas şiddetindeki morfolojik değişimler belirlenmiştir. Elde edilen bu yansıma değerleri matematiksel formüllerle ifade edilen vejetasyon indisi değerlerine dönüştürülerek verim tahminlerinin belirlenmesinde kullanılabilir hale getirilmiştir. Elde edilen sonuçlar dikkate alındığında, Kenanbey çeşidi (15 Haziran 2019, Feekes 10.5.4) hariç, tüm ekmeklik çeşitler için özellikle erken çiçeklenme döneminde (25 Mayıs 2019, Feekes 10.5.1) hesaplanan spektral indekslerin verim tahmininde etkili olduğu belirlenmiştir. Tüm ekmeklik ve makarnalık çeşitlerde verimi tahmin etmek için belirlenen tüm indeksleri içeren ve çiçeklenme başlangıcı olan 25 Mayıs 2019 (Feekes 10.5.1) tarihli spektral bant bölgesinin etkili olduğu tespit edilmiştir. Tane verimi tahmininde erken çiçeklenme başlangıç döneminden (Feekes 10.5.1) başlayarak tane bağlama dönemi (Feekes 10.5.3) ve süt olum dönemine (Feekes 10.5.4) doğru spektral indekslerin korelasyon değerlerinde azalma olduğu tespit edilmiştir. Elde edilen bu indeks değerleri ile verim değerleri arasındaki korelasyonlar incelendiğinde öne çıkan fenolojik dönemler ve bu dönemlere ilişkin yüksek korelasyona sahip indekslerinin hesaplanabildiği belirlenmiştir. Günümüzde artık geleneksel hastalık takip yöntemlerinin yerine optik sensör teknolojisinin kullanımı ile verim tahmininde multispektral ve hiperspektral kameraların üzerinde yer aldığı insansız hava araçları ile alınan görüntülerin yapay zeka ve derin öğrenme teknikleri kullanılarak yersel verilerle doğrulanması sonucu erken dönemde hızlı ve doğru bir şekilde verim tahminine yönelik yeni yaklaşımların geliştirilmesinin yolu açılmıştır.

Keywords

Buğday, hiperspektral veri, sarı pas (Puccinia striiformis f. sp. tritici) hastalığı, spektral indeks, verim tahmini

Project Number

YÖK Thesis No: 671046/Date: 25.05.2021.

Investigation of Disease-Yield Relationship of Yellow Rust in Some Bread and Durum Wheat Varieties by Phenological Periods Using Hyperspectral Data

Abstract

The aim of this study was to evaluate the severity of yellow rust in different phenological periods by
subjecting bread (Bayraktar 2000, Demir 2000, Eser and Kenanbey) and durum (Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000) wheat varieties to different spore doses (0%, 25%, 50% and 100%) under controlled epidemic conditions. The research was conducted in Yenimahalle, Ankara, Turkey during the 2018-2019 growing season. In the study, the morphological changes in yellow rust severity were determined at different phenological developmental stages of the test materials with the reflectance values obtained by using handheld spectroradiometer in different spore dose applications during the period from tillering to stalk emergence. These reflectance values were converted into vegetation index values expressed by mathematical formulae and used in determining yield estimates. Considering the results obtained, it was determined that the spectral indices calculated especially in the early flowering period (25 May 2019, Feekes 10.5.1) were effective in yield estimation for all bread varieties except Kenanbey variety (15 June 2019, Feekes 10.5.4). It was determined that the spectral band region of 25 May 2019 (Feekes 10.5.1), which includes all indices determined to predict yield in all bread and durum varieties and which is the beginning of flowering, was effective. In grain yield estimation, it was determined that there was a decrease in the correlation values of the spectral indices starting from the early flowering period (Feekes 10.5.1) towards the grain setting period (Feekes 10.5.3) and milk maturity period
(Feekes 10.5.4). When the correlations between these index values and yield values were examined, it was
determined that prominent phenological periods and high correlation indices could be calculated for these periods. Nowadays, with the use of optical sensor technology instead of traditional disease surveillance methods, the way has paved the way for the development of new approaches for early, fast and accurate yield estimation as a result of the verification of images taken by unmanned aerial vehicles on which multispectral and hyperspectral cameras are located with ground data using artificial intelligence and deep learning techniques.

Keywords

Hyperspectral data, spectral indice, wheat, yellow rust desease (Puccinia striiformis f. sp. tritici), yield estimate

Supporting Institution

The Field Crop Research Center Institute in ANKARA ,The Soil, Fertilizer and Water Resources Central Resarch Institute

Project Number

YÖK Thesis No: 671046/Date: 25.05.2021.

Thanks

This study was carried out by Metin AYDOĞDU in the master's thesis "Determination of the Seasonal Effects of Different Iron and Zinc Applications on Yellow Rust (Puccinia striiformis f. sp. tritici) Disease in Winter Wheat Using Multi-Band Data" at Kırşehir Ahi Evran University, Institute of Science, Department of Agricultural Biotechnology. (YÖK Thesis No: 671046/Date: 25.05.2021).We would like to thank the Department of Plant Diseases of the Central Research Institute of Agricultural Control who contributed to the preparation and execution of the thesis, to Dr. Nilüfer AKCI, Thesis Jury members; from Harran University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition Prof.Dr. Hikmet GÜNAL to Dr. Nurullah ACİR from Kırşehir Ahi Evran University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition for his contributions.

References

• Akan, K. 2019. Sarı Pas (Puccinia striiformis f. sp. tritici) hastalığına dayanıklı makarnalık buğday hatlarının geliştirilmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 6 (4): 661-670. DOI: https://doi.org/ 10.30910/turkjans.633548 (in Turkish).
• Aktaş, H., Morgunov A., Karaman, M., and Kılıc, H., Kendal, E. 2012. Evaluating of yield losses caused by yellow rust pressure in some bread wheat genotypes. In 13th International Cereal Rusts and Powdery Mildews Conference (p. 16).
• Anonymous, 2022. Meteoroloji Genel Müdürlüğü Resmi İstatistikler. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=A&m=ANKARA. (Data accessed May 5, 2020).
• Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L. and Royo, C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92(1):83-91.
• Asseng, S., Foster, I. A. N. and Turner, N. C. 2011. The impact of temperature variability on wheat yields. Global Change Biology, 17(2): 997-1012.
• Baret, F., and Guyot, G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2-3): 161-173.
• Braun, H.J. and 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.
• Broge, N.H. and Leblanc, E., 2001. Comparing prediction power and stability of broad-band and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76: 156-172. DOI:http://dx.doi.org/10.1016/S0034-4257(00)00197-8
• Chen, J.M. and Cihlar, J. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images, Remote Sensing of Environment, 55 (2): 153-162. DOI: https://doi.org/10.1016/0034-4257(95)00195-6
• Chen, X. M. 2005. Epidemiology and control of stripe rust [Puccinia striiformis f. sp. tritici] on wheat. Canadian Journal of Plant Pathology, 27(3): 314-337, DOI: http://dx.doi.org/10.1080/07060660509507230
• Curtis, B.C. 2002. Wheat in the World, In: Curtis, B.C., Rajaram, S. and Macpherson, H.G., Eds., Bread Wheat Improvement and Production, Plant Production and Protection Series 30, FAO, Roma, 1-18.
• Daughtry, C. S.T., Walthall, C. L., Kim, M. S., Brown De Colstoun, E. B., and McMurtrey III, J. E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2): 229-239 DOI: https://doi.org/10.1016/S0034-4257(00)00113-9
• Delwiche, S. R., and Kim, M. S. 2000. Hyperspectral imaging for detection of scab in wheat. Biological Quality and Precision Agriculture II. International Society for Optics and Photonics, 4203: 13–20. DOI: http://dx.doi.org/10.1117/12.411752
• Devadas, R., Lamb, D. W., Simpfendorfer, S., and Backhouse, D. 2009. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10(6): 459-470.
• Duveiller, G., Weiss, M., Baret, F. and Defourny, P. 2011. Retrieving wheat green area index during the growing season from optical time series measurements based on neural network radiative transfer inversion. Remote Sensing of Environment, 115 (3): 887-896.
• Eversmeyer, M. G., and 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.
• Fabbri, C., Napoli, M., Verdi, L., Mancini, M., Orlandini, S., and Dalla Marta, A. 2020. A sustainability assessment of the greenseeker n management tool: A lysimetric experiment on barley. Sustainability, 12(18): 7303.
• Filella, I., Serrano, L., Serra, J. and Peñuelas, J., 1995. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35: 1400-1405.
• Gamon, J. A., Peñuelas, J., and Field, C. B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1): 35-44.
• Gitelson, A. and Merzlyak, M.N. 1994. Spectral reflectance changes associate with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143: 286-292.
• Gitelson, A.A., Kaufman, Y.J. and Merzlyak, M.N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3): 289-298.
• Gitelson, A. A., Merzlyak, M. N., and Chivkunova, O. B. 2001. Optical properties and nondestructive estimation anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1): 38-45.
• Goel, N. S., and Qin, W. 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sensing Reviews, 10(4): 309-347.
• Goetz, A.F., Vane, G., Solomon, J.E., Rock, B.N. 1985. Imaging spectrometry for Earth remote sensing. Science (New York, N.Y.). 228(4704):1147-1153. DOI: https://doi.org/ 10.1126/science.228.4704.1147.
• Gupta, R. K., Vijayan, D., and Prasad, T. S. 2001. New hyperspectral vegetation characterization parameters. Advances in Space Research, 28(1): 201-206.
• Gündoğdu, K. S., & Bantchina, B. B. (2018). Landsat uydu görüntülerinden NDVI değer dağılımının parsel bazlı değerlendirilmesi, Uludağ üniversitesi ziraat fakültesi çiftlik arazisi örneği. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi, 32(2): 45-53.
• Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J. and Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sensing of Environment, 81(2-3): 416-426.
• Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., and Strachan, I. B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3): 337-352.
• Huang, W., Guan, Q., Luo, J., Zhang, J., Liang, D., Huang, L. and Zhang, D. 2014. New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens., 7 (6): 2516-2524.
• IBM Corp. 2014. IBM SPSS Statistics for Windows, Version 22.0. IBM Corp., Armonk, NY. Jordan, C.F. 1969. Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663-666. DOI: https://doi.org/10.2307/1936256
• Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., McMurtrey, J.E., and Walthall, C. L. 1994. The use of high spectral resolution bants for estimating absorbed photosynthetically active radiation (APAR). In CNES Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, Val d’Isére, France, 17-21 January 1994, pp. 299-306.
• Kumhálová, J. and Matějková, Š. 2017. Yield variability prediction by remote sensing sensors with different spatial resolution. International Agrophysics, 31(2): 195-202.
• Large, E. C. 1954. Growth Stages in Cereals Illustration of the Feekes Scale. Plant Pathology. 3 (4): 128–129. DOI: https://doi.org/10.1111/j.1365-3059.1954.tb00716.x
• Li, G.B., Zeng, S. M. and Li, Z.Q. 1989. Integrated management of wheat pests (pp. 185-186). Beijing: Press of Agriculture Science and Technology of Chine
• Liu, L., Dong, Y., Huang, W., Du, X., Ren, B., Huang, L., ... and Ma, H. 2020. A disease index for efficiently detecting wheat Fusarium head blight using sentinel-2 multispectral imagery. IEEE Access, 8: 52181-52191
• Mert, Z., Karakaya, A., Düşünceli, F., Akan, K., and Çetin, L. 2012. Determination of Puccinia graminis f. sp. tritici races of wheat in Turkey. Turkish Journal of Agriculture and Forestry, 36(1): 107-120.
• Merton, R., and Huntington, J. 1999. Early simulation results of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. Proceedings of the Eighth Annual JPL Airborne Earth Science Workshop. NASA, Jet Propulsion Laboratory, Pasadena, California, USA. 8 -14 February 1999. Pasadena, CA, USA (pp. 9-11).
• Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., and Rakitin, V.Y. 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1):135–141. DOI: https://doi.org/10.1034/j.1399-3054.1999.106119.x
• Moriondo, M., Maselli, F., Bindi, M. 2007. A simple model of regional wheat yield based on NDVI data, European Journal of Agronomy., 26:266–274.
• Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A. and 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.
• Murray, G., Wellings, C., Simpfendorfer, S., and Cole, C. 2005. Stripe rust: Understanding the disease in wheat. Manag. Guid., 1–12.
• Naqvi, S.M.Z.A., Tahir, M.N., Shah, G.A., Sattar, R.S., and Awais, M. 2019. Remote estimation of wheat yield based on vegetation indices derived from time series data of Landsat 8 imagery. Applied Ecology and Environmental Research, 17(2): 3909-3925.
• Oppelt, N. and Mauser, W., 2004. Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing, 25 (1): 145-159.
• Peñuelas, J., Baret, F., and Filella, I. 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2): 221-230.
• Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J. and Field, C.B. 1994, Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves, Remote Sensing of Environment, 48(2): 135-146.
• Peñuelas, J., Pinol, R.O., Ogaya, R. and Filella, I. 1997. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18: 2869–2875.
• Peterson, R.F. Campbell, A.B. and 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.
• Prasad, A.K., Chai, L., Singh, R.P., and Kafatos, M. 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. Journal of Applied Earth Observation and Geoinformation, 8: 26–33.
• Roelfs, A. P. 1978. Estimated losses caused by rust in small grain cereals in the United States, 1918-76 (Vol. 1356). Department of Agriculture, Agricultural Research Service.
• Roelfs, A.P., Singh, R.P. and Saari, E.E. 1992. Rust diseases of wheat: Concepts and methods of disease management, CIMMYT, Mexico, 80 pp.
• Roujean, J. L., and Breon, F. M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3): 375-384.
• Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication 351(1): 309.
• 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.
• Strange, R. N., and Scott, P. R. 2005. Plant disease: A threat to global food security. Annual Review of Phytopathology, 43(1): 83-116.
• Sultana, S.R. Ali, A., Ahmad, A., Mubeen, M., Zia-Ul-Haq, M., Ahmad, S., Ercisli, S., Jaafar, H.Z.E. 2014. Normalized difference vegetation index as a tool for wheat yield estimation: A case study from Faisalabad, Pakistan. The Scientific World Journal, 2014.
• Thenkabail, P. S., Smith, R. B., and De Pauw, E. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2): 158-182.
• Vergara-Diaz, O., Kefauver, S. C., Elazab, A., Nieto-Taladriz, M. T., and Araus, J. L. (2015). Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions. The Crop Journal, 3(3): 200-210.
• Watkins, J. E. 2006. Leaf, stem and stripe rust diseases of wheat. Neb Guide: University of Nebraska-Lincoln.
• Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., and Popham, T. W. 2005. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 47(2): 121-135.
• Yu, K., Anderegg, J., Mikaberidze, A., Karisto, P., Mascher, F., McDonald, B.A., Achim, W., and Hund, A. 2018. Hyperspectral canopy sensing of wheat Septoria tritici blotch disease. Frontiers in Plant Science, 9: 1195. DOI: https://doi.org/10.3389/fpls.2018.01195.
• Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., González, M., and De Frutos, A., 2005. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99 (3): 271-287.