Research Article
BibTex RIS Cite

Bazı Ekmeklik ve Makarnalık Buğday Çeşitlerinde Spektral Bant Bölgeleri Kullanılarak Sarı Pas Hastalık Reaksiyonlarının Değerlendirilmesi

Year 2023, Volume: 4 Issue: 2, 166 - 186, 28.09.2023
https://doi.org/10.48123/rsgis.1198224

Abstract

Sarı pas (Etmen: Puccinia striiformis f. sp. tritici) hastalığı, buğday da üretim ve kaliteyi olumsuz yönde etkileyen önemli fungal bir hastalıktır. Bu araştırmanın amacı; test materyallerine farklı dozlarda (%0, %25, %50, %100) uygulanan sarı pas hastalığına karşı bitkinin gösterdiği reaksiyonların spektral özellikler yardımıyla belirlenmesi ve hastalığın mevsim içindeki değişimini etkileyen spektral bant bölgelerinin ortaya çıkarılmasıydı. Çalışma kapsamında, Ekmeklik çeşitler için; Eser, Bayraktar 2000 ve Demir 2000 çeşitleri erken-orta dönemde, Kenanbey çeşidi ise orta-geç dönemde yüksek korelasyon göstermiştir. Bütün ekmeklik çeşitler için etkili bant bölgesi olan Kırmızı+Kırmızı Kenar+ Yakın Kızıl Ötesi (NIR) aralığında, Kenanbey çeşidi ise NIR aralığında hastalık şiddeti değerlerinde artış (+) göstermiştir. Makarnalık çeşitler için; Eminbey ve Çeşit-1252 çeşitleri erken dönemde, Mirzabey 2000 çeşidi erken-orta dönemde, Kızıltan 91 çeşidi ise orta-geç dönemde yüksek korelasyon göstermiştir. Kızıltan 91 çeşidi ise Kırmızı+Kırmızı Kenar+NIR bölgede, Çeşit-1252 çeşidi Yeşil+Kırmızı bölgede, Eminbey ve Mirzabey 2000 çeşidi Yeşil+Kırmzı+Kırmızı Kenar bölgedeki, bant aralıklarında etkili olmuş ve hastalık şiddeti reaksiyonlarında artış (+) göstermiştir.

References

  • Abburu, S., & Golla, S. B. (2015). Satellite image classification methods and techniques: A review. International Journal of Computer Applications, 119(8), 20-24.
  • 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.
  • Aktaş, H., Karaman, M., Tekdal, S., Kılıc, H., & Kendal, E. (2012, August). Evaluating of yield losses caused by yellow rust pressure in some bread wheat genotypes. In 13th International Cereal Rusts and Powdery Mildews Conference, 2012. Proceedings. (pp. 16).
  • Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84(2), 137-145.
  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156-172.
  • Chen, J. M., & Cihlar, J. (1996). Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55(2), 153-162.
  • 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.
  • Delwiche, S. R., & Kim, M. S. (2000, December). Hyperspectral imaging for detection of scab in wheat. In Biological Quality and Precision Agriculture II, 2000. Proceedings. (pp. 13-20). SPIE.
  • Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse, D. (2009). Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10, 459-470.
  • Dusunceli, F., Cetin, L., Albustan, S., & Beniwal, S. P. S. (1996, September). Occurrence and impact of wheat stripe rust (Puccinia striiformis) in Turkey in 1994/95 crop season. In 9th European and Mediterranean Cereal Rusts and Powdery Mildews Conference, 1996. Proceedings. (pp. 309).
  • Duveiller, G., Weiss, M., Baret, F., & 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.
  • FAO. (2020) FAOSTAT Statistical Database. Retrieved from https://www.fao.org/faostat/en/#home.
  • Feng, W., Shen, W., He, L., Duan, J., Guo, B., Li, Y., Wang, C., & Guo, T. (2016). Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices. Precision Agriculture, 17, 608-627.
  • Filella, I., Serrano, L., Serra, J., & Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35(5), 1400-1405.
  • Gitelson, A. A., Kaufman, Y. J., & 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., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45.
  • Gitelson, A., & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3), 286-292.
  • Goetz, A. F., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147-1153.
  • Gupta, R. K., Vijayan, D., & Prasad, T. S. (2001). New hyperspectral vegetation characterization parameters. Advances in Space Research, 28(1), 201-206.
  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & 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.
  • Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & 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.
  • Hatfield, P. L., & Pinter Jr, P. J. (1993). Remote sensing for crop protection. Crop Protection, 12(6), 403-413.
  • Huang, W., Guan, Q., Luo, J., Zhang, J., Zhao, J., Liang, D., Huang, L., & Zhang, D. (2014). New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2516-2524.
  • Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J. (2007). Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8, 187-197.
  • Kavzoglu, T., & Reis, S. (2008). Performance analysis of maximum likelihood and artificial neural network classifiers for training sets with mixed pixels. GIScience & Remote Sensing, 45(3), 330-342.
  • Kim, M. S., Daughtry, C. S. T., Chappelle, E. W., McMurtrey, J. E., & Walthall, C. L. (1994). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par). In 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, 1994. Proceedings. (pp. 299-306). CNES.
  • Large, E. C. (1954). Growth stages in cereals. Illustration of the Feekes scale. Plant Pathology, 3, 128-129.
  • Liu, L., Dong, Y., Huang, W., Du, X., Ren, B., Huang, L., Zheng Q., & Ma, H. (2020). A disease index for efficiently detecting wheat fusarium head blight using Sentinel-2 multispectral imagery. IEEE Access, 8, 52181-52191.
  • Merton, R., & Huntington, J. (1999, February). Early simulation results of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In Eighth Annual JPL Airborne Earth Science Workshop, 1999. Proceedings. (pp. 9-11). NASA.
  • Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1), 135-141.
  • 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 Kohonen maps. Real-Time Imaging, 11(2), 75-83.
  • 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.
  • 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(1), 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. (1995). Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology, 33(1), 489-528.
  • Oppelt, N., & 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., Gamon, J. A., Fredeen, A. L., Merino, J., & 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, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869-2875.
  • 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(5), 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. In A. P. Roelfs (Eds.), Estimated losses caused by rust in small grain cereals (pp. 1356-1372). Washington DC: 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.
  • Samborski, D. J. (1985). Wheat leaf rust. In A.P. Roelfs & W.R. Bushnell (Eds.), Diseases, distribution, epidemiology, and control (pp. 39-59). Cambridge, Massachusetts: Academic Press.
  • Strange, R. N., & Scott, P. R. (2005). Plant disease: a threat to global food security. Annual Review of Phytopathology, 43(1), 83-116.
  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158-182.
  • Ustuner, M., Sanli, F. B., & Dixon, B. (2015). Application of support vector machines for land use classification using high-resolution rapideye images: A sensitivity analysis. European Journal of Remote Sensing, 48(1), 403-422.
  • Yu, K., Anderegg, J., Mikaberidze, A., Karisto, P., Mascher, F., McDonald, B. A., Walter, A., & Hund, A. (2018). Hyperspectral canopy sensing of wheat Septoria tritici blotch disease. Frontiers in Plant Science, 9, 1195. doi: 10.3389/fpls.2018.01195.
  • Zadoks, J.C., Chang, T.T. and Konzak, C.F. (1974) A decimal code for the growth stages of cereals. Weed Research, 14, 415- 21.
  • Zarco-Tejada, P. J., Berjón, A., Lopez-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., González, M. R., & 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.
  • Zhang, J. C., Pu, R. L., Wang, J. H., Huang, W. J., Yuan, L., & Luo, J. H. (2012a). Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Computers and Electronics in Agriculture, 85, 13-23.
  • Zhang, J., Huang, W., Li, J., Yang, G., Luo, J., Gu, X., & Wang, J. (2011). Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat. Precision Agriculture, 12, 716-731.
  • Zhang, J., Pu, R., Huang, W., Yuan, L., Luo, J., & Wang, J. (2012b). Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crops Research, 134, 165-174.
  • Zhao, C., Huang, M., Huang, W., Liu, L., & Wang, J. (2004). Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models. In IEEE International Geoscience and Remote Sensing Symposium, 2004. Proceedings. (pp. 4318-4320). IEEE.

Evaluation of Yellow Rust Reactions in some Bread and Durum Wheat Varieties by Using Spectral Band Regions

Year 2023, Volume: 4 Issue: 2, 166 - 186, 28.09.2023
https://doi.org/10.48123/rsgis.1198224

Abstract

Yellow rust (caused by Puccinia striiformis f. sp. tritici) is an important fungal disease affecting wheat production and quality. The purpose of this study was to identify the spectral band regions that influence how the disease changes throughout the year by determining how the plant responds to yellow rust when it is applied to test materials at various doses (0%, 25%, 50%, and 100%). Eser, Bayraktar 2000 and Demir 2000 varieties showed high correlation in the early-mid period of the study for bread varieties, while Kenanbey variety exhibited high correlation in the mid-late period. Effective band region for all bread types are The Red+Red Edge+ Near Infrared (NIR) range and NIR range of the Kenanbey variety both showed an increase (+) in disease severity values. Eminbey and Çeşit-1252 varieties for durum varieties displayed high correlation in the early period, followed by Mirzabey 2000 variety in the early to medium period and Kızıltan-91 variety the mid to late period. Kızıltan 91 variety in Red+Red Edge+NIR region, Çeşit-1252 variety in Green+Red region, Eminbey and Mirzabey 2000 varieties in Green+Red+Red Edge were effective in band ranges in the region and showed an increase (+) in disease severity reactions.

References

  • Abburu, S., & Golla, S. B. (2015). Satellite image classification methods and techniques: A review. International Journal of Computer Applications, 119(8), 20-24.
  • 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.
  • Aktaş, H., Karaman, M., Tekdal, S., Kılıc, H., & Kendal, E. (2012, August). Evaluating of yield losses caused by yellow rust pressure in some bread wheat genotypes. In 13th International Cereal Rusts and Powdery Mildews Conference, 2012. Proceedings. (pp. 16).
  • Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84(2), 137-145.
  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156-172.
  • Chen, J. M., & Cihlar, J. (1996). Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55(2), 153-162.
  • 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.
  • Delwiche, S. R., & Kim, M. S. (2000, December). Hyperspectral imaging for detection of scab in wheat. In Biological Quality and Precision Agriculture II, 2000. Proceedings. (pp. 13-20). SPIE.
  • Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse, D. (2009). Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10, 459-470.
  • Dusunceli, F., Cetin, L., Albustan, S., & Beniwal, S. P. S. (1996, September). Occurrence and impact of wheat stripe rust (Puccinia striiformis) in Turkey in 1994/95 crop season. In 9th European and Mediterranean Cereal Rusts and Powdery Mildews Conference, 1996. Proceedings. (pp. 309).
  • Duveiller, G., Weiss, M., Baret, F., & 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.
  • FAO. (2020) FAOSTAT Statistical Database. Retrieved from https://www.fao.org/faostat/en/#home.
  • Feng, W., Shen, W., He, L., Duan, J., Guo, B., Li, Y., Wang, C., & Guo, T. (2016). Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices. Precision Agriculture, 17, 608-627.
  • Filella, I., Serrano, L., Serra, J., & Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35(5), 1400-1405.
  • Gitelson, A. A., Kaufman, Y. J., & 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., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74(1), 38-45.
  • Gitelson, A., & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3), 286-292.
  • Goetz, A. F., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147-1153.
  • Gupta, R. K., Vijayan, D., & Prasad, T. S. (2001). New hyperspectral vegetation characterization parameters. Advances in Space Research, 28(1), 201-206.
  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & 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.
  • Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & 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.
  • Hatfield, P. L., & Pinter Jr, P. J. (1993). Remote sensing for crop protection. Crop Protection, 12(6), 403-413.
  • Huang, W., Guan, Q., Luo, J., Zhang, J., Zhao, J., Liang, D., Huang, L., & Zhang, D. (2014). New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2516-2524.
  • Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J. (2007). Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8, 187-197.
  • Kavzoglu, T., & Reis, S. (2008). Performance analysis of maximum likelihood and artificial neural network classifiers for training sets with mixed pixels. GIScience & Remote Sensing, 45(3), 330-342.
  • Kim, M. S., Daughtry, C. S. T., Chappelle, E. W., McMurtrey, J. E., & Walthall, C. L. (1994). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par). In 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, 1994. Proceedings. (pp. 299-306). CNES.
  • Large, E. C. (1954). Growth stages in cereals. Illustration of the Feekes scale. Plant Pathology, 3, 128-129.
  • Liu, L., Dong, Y., Huang, W., Du, X., Ren, B., Huang, L., Zheng Q., & Ma, H. (2020). A disease index for efficiently detecting wheat fusarium head blight using Sentinel-2 multispectral imagery. IEEE Access, 8, 52181-52191.
  • Merton, R., & Huntington, J. (1999, February). Early simulation results of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In Eighth Annual JPL Airborne Earth Science Workshop, 1999. Proceedings. (pp. 9-11). NASA.
  • Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1), 135-141.
  • 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 Kohonen maps. Real-Time Imaging, 11(2), 75-83.
  • 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.
  • 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(1), 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. (1995). Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology, 33(1), 489-528.
  • Oppelt, N., & 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., Gamon, J. A., Fredeen, A. L., Merino, J., & 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, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869-2875.
  • 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(5), 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. In A. P. Roelfs (Eds.), Estimated losses caused by rust in small grain cereals (pp. 1356-1372). Washington DC: 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.
  • Samborski, D. J. (1985). Wheat leaf rust. In A.P. Roelfs & W.R. Bushnell (Eds.), Diseases, distribution, epidemiology, and control (pp. 39-59). Cambridge, Massachusetts: Academic Press.
  • Strange, R. N., & Scott, P. R. (2005). Plant disease: a threat to global food security. Annual Review of Phytopathology, 43(1), 83-116.
  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158-182.
  • Ustuner, M., Sanli, F. B., & Dixon, B. (2015). Application of support vector machines for land use classification using high-resolution rapideye images: A sensitivity analysis. European Journal of Remote Sensing, 48(1), 403-422.
  • Yu, K., Anderegg, J., Mikaberidze, A., Karisto, P., Mascher, F., McDonald, B. A., Walter, A., & Hund, A. (2018). Hyperspectral canopy sensing of wheat Septoria tritici blotch disease. Frontiers in Plant Science, 9, 1195. doi: 10.3389/fpls.2018.01195.
  • Zadoks, J.C., Chang, T.T. and Konzak, C.F. (1974) A decimal code for the growth stages of cereals. Weed Research, 14, 415- 21.
  • Zarco-Tejada, P. J., Berjón, A., Lopez-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., González, M. R., & 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.
  • Zhang, J. C., Pu, R. L., Wang, J. H., Huang, W. J., Yuan, L., & Luo, J. H. (2012a). Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Computers and Electronics in Agriculture, 85, 13-23.
  • Zhang, J., Huang, W., Li, J., Yang, G., Luo, J., Gu, X., & Wang, J. (2011). Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat. Precision Agriculture, 12, 716-731.
  • Zhang, J., Pu, R., Huang, W., Yuan, L., Luo, J., & Wang, J. (2012b). Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crops Research, 134, 165-174.
  • Zhao, C., Huang, M., Huang, W., Liu, L., & Wang, J. (2004). Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models. In IEEE International Geoscience and Remote Sensing Symposium, 2004. Proceedings. (pp. 4318-4320). IEEE.
There are 53 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Metin Aydoğdu 0000-0001-6920-1976

Kadir Akan 0000-0002-1612-859X

Early Pub Date September 26, 2023
Publication Date September 28, 2023
Submission Date November 2, 2022
Acceptance Date July 2, 2023
Published in Issue Year 2023 Volume: 4 Issue: 2

Cite

APA Aydoğdu, M., & Akan, K. (2023). Evaluation of Yellow Rust Reactions in some Bread and Durum Wheat Varieties by Using Spectral Band Regions. Türk Uzaktan Algılama Ve CBS Dergisi, 4(2), 166-186. https://doi.org/10.48123/rsgis.1198224