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Identification of Spectral Bands to Discriminate Wheat Spot Blotch using in Situ Hyperspectral Data

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Abstract

The present study is focused on electromagnetic spectra generated in the hyperspectral domain to detect and discriminate healthy wheat crops from those infected with spot blotch. For discriminating the spot blotch-affected wheat crop from the healthy one, the ground observation with hyperspectral signature was collected employing a handheld spectroradiometer. Data on canopy reflectance were obtained using an analytical spectral device spectroradiometer at separate sampling intervals over the spectral region of 350–2500 nm (resampled at 1 nm intervals) over all randomly selected sites at Khoribari (26o33′36″N; 88o11′75″E), West Bengal, India. Significance was certainly higher between healthy and spot blotch-affected hyper-spectra with red-edge (693–743 nm) and SWIR-2 (1475–1525 nm) with green-1 (510 nm) also exhibiting some significance that provided a clear difference in curve of healthy and different levels of spot blotch-affected wheat crop. The difference was spectacular for both levels of significance (2 × 10–16) and in detecting the spot blotch-affected wheat crop for red-edge (band center: 731 nm; band width: 10 nm). This study unravels vistas for investigating discrimination of spot blotch disease by sharper use of the band center (731 nm) of the red-edge band to develop precise wheat spot blotch disease stress index clearly different from other biotic and abiotic signatures. For the first time, the spectral characterization of spot blotch disease of wheat in India was done using ground-based hyperspectral data. This will allow the growers to make need-based timely effective application of fungicides prior to  the inception of the disease.

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References

  • Ambrus, A., Burai, P., Lénárt, C., Enyedi, P., & Kovács, Z. (2015). Estimating biomass of winter wheat using narrowband vegetation indices for precision agriculture. Journal of Central European Green Innovation, 3, 13–22.

    Google Scholar 

  • Ashourloo, D., Mobasheri, M. R., & Huete, A. (2014). Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing, 6, 4723–4740.

    Article  Google Scholar 

  • Bebronne, R., Carlier, A., Meurs, R., Leemans, V., Vermeulen, P., Dumont, B., & Mercatoris, B. (2020). In-field proximal sensing of Septoria tritici blotch, stripe rust and brown rust in winter wheat by means of reflectance and textural features from multispectral imagery. Biosystems Engineering, 197, 257–269. https://doi.org/10.1016/j.biosystemseng.2020.06.011

    Article  Google Scholar 

  • Bendel, N., Kicherer, A., Backhaus, A., Klück, H. C., Seiffert, U., Fischer, M., Voegele, R. T., & Töpfer, R. (2020). Evaluating the suitability of hyper and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards. Plant Methods, 16, 142. https://doi.org/10.1186/s13007-020-00685-3

    Article  Google Scholar 

  • Bock, C. H., Barbedo, J. G. A., Ponte, E. M. D., Bohnenkamp, D., & Mahlein, A. K. (2020). From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy. Phytopathology Research, 2, 9–38. https://doi.org/10.1186/s42483-020-00049-8

    Article  Google Scholar 

  • Cao, X., Luo, Y., Zhou, Y., Fan, J., Xu, X., West, J. S., Duan, X., & Cheng, D. (2015). Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance. PLoS ONE, 10(3), e0121462. https://doi.org/10.1371/journal.pone.0121

    Article  Google Scholar 

  • Chauhan, S., Darvishzadeh, R., Lu, Y., Boschetti, M., & Nelson, A. (2020). Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, 243, 111804. https://doi.org/10.1016/j.rse.2020.111804

    Article  Google Scholar 

  • Chowdhury, A. K. (2021). Threatening wheat diseases in the eastern Gangetic plains: the current status of disease resistance. Indian Phytopathology. https://doi.org/10.1007/s42360-021-00336-0

    Article  Google Scholar 

  • Chowdhury, A. K., Singh, G., Tyagi, B. S., Bhattacharya, P. M., & Singha Roy, A. K. (2008). Assessment of wheat (Triticum aestivum) cultivars to boron deficiency-induced spike sterility and its impact on grain yield under terai region of West Bengal. Indian Journal of Agricultural Sciences, 78, 834–837.

    Google Scholar 

  • Croft, H., & Chen, J. M. (2018). Leaf Pigment Content. Comprehensive Remote Sensing. https://doi.org/10.1016/b978-0-12-409548-9.10547-0

    Article  Google Scholar 

  • Curran, P. J. (1989). Remote Sensing of Foliar Chemistry. Remote Sensing of Environment, 30, 271–278.

    Article  Google Scholar 

  • Datt, B., & Paterson, M. (2000). Vegetation-soil spectral mixture analysis. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 5, 1936–1938.

    Google Scholar 

  • Duveiller, E., & Dubin, H. J. (2002). Helminthosporium leaf blights: spot blotch and tan spot. In B. C. S. H. CurtisRajaramGomez Macphercon (Ed.), Bread wheat Improvement and Production Series. Rome: F.A.O.

    Google Scholar 

  • Duveiller, E., Kandel, Y. R., Sharma, R. C., & Shrestha, S. M. (2005). Epidemiology of foliar blights (spot blotch and tan spot) of wheat in the plains bordering the Himalayas. Phytopathology, 95, 248–256.

    Article  Google Scholar 

  • Eyal, Z.; Scharen, A.L.; Prescott, J.M. & van Ginkel, M. (1987). The Septoria Diseases of Wheat: Concepts and Methods of Disease Management. CIMMYT, Mexico DF, Mexico (ISBN 968-6127-06-2), p. 46

  • Fagerland, M. W., & Sandvik, L. (2009). Performance of five two-sample location tests for skewed distributions with unequal variances. Contemporary Clinical Trials, 30, 490–496.

    Article  Google Scholar 

  • Fahrentrapp, J., Ria, F., Geilhausen, M., & Panassiti, B. (2019). Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2019.00628

    Article  Google Scholar 

  • Gazala, I. F. S., Sahoo, R. N., Pandey, R., Mandal, B., Gupta, V. K., Singh, R., & Sinha, P. (2013). Spectral reflectance pattern in soybean for assessing yellow mosaic disease. Indian Journal of Virology, 24, 242–249.

    Article  Google Scholar 

  • Gianessi, L.P. (2014). Importance of pesticides for growing wheat in South Asia. International Pesticide Benefit Case Study 106. CropLife Foundation. Downloaded from https://croplife.org/case-study/importance-of-pesticides-for-growing-wheat-in-south-asia/ on 07 Nov 2020, 1029 hrs (IST)

  • GoI (Government of India). (2022). First Advance Estimates of Production of Foodgrains for 2020–21. Ministry of Agriculture and Farmers Welfare Department of Agriculture, Cooperation and Farmers’ Welfare, Government of India, downloaded from https://eands.dacnet.nic.in/Advance_Estimate/Time%20Series%201%20AE%202021-22%20(English).pdf on 04 Feb 2022 at 1422 hrs (IST).

  • Government of West Bengal. (2016). Districtwise Estimates of Yield Rate and Production of Nineteen Major Crops of West Bengal during 2014–15. Bureau of Applied Economics and Statistics, Department of Statistics and Program Implementation, Government of West Bengal, p 92 + annexure (p. 151).

  • Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., & Ruan, C. (2020). Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sensing, 12, 1419–1436. https://doi.org/10.3390/rs12091419

    Article  Google Scholar 

  • Gupta, P. K., Chand, R., Vasistha, N. K., Pandey, S. P., Kumar, U., Mishra, V. K., & Joshi, A. K. (2017). Spot blotch disease of wheat: Current status of research on genetics and plant breeding. Plant Pathology. https://doi.org/10.1111/ppa.12781

    Article  Google Scholar 

  • Heim, R. H. J., Wright, I. J., Allen, A. P., Geedicke, I., & Oldeland, J. (2019). Developing a spectral disease index for myrtle rust (Austropuccinia psidii). Plant Pathology, 68, 738–745.

    Article  Google Scholar 

  • Huang, H., Deng, J., Lan, Y., Yang, A., Zhang, L., Wen, S., Zhang, H., Zhang, Y., & Deng, Y. (2019). Detection of Helminthosporium leaf blotch disease based on UAV imagery. Applied Sciences, 9, 558–569. https://doi.org/10.3390/app9030558

    Article  Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.

    Article  Google Scholar 

  • Jamil, M., Ali, N., Ali, A., & Mujeeb-Kazi, A. (2020). Spot blotch in bread wheat: virulence, resistance, and breeding perspectives. In M. Ozturk & A. Gul (Eds.), Climate Change and Food Security with Emphasis on Wheat (pp. 217–228). Academic Press. https://doi.org/10.1016/b978-0-12-819527-7.00014-5

    Chapter  Google Scholar 

  • Krezhova, B., Dikova, B., & Maneva, S. (2014). Ground based hyperspectral remote sensing for disease detection of tobacco plants. Bulgarian Journal of Agricultural Science, 20, 1142–1150.

    Google Scholar 

  • Krishna, G., Sahoo, R. N., Pargal, S., Gupta, V. K., Sinha, P., Bhagat, S., Saharan, M. S., Singh, R., & Chattopadhyay, C. (2014). Assessing wheat yellow rust disease through hyperspectral remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 1413–1416.

    Article  Google Scholar 

  • Kumar, J., Schaer, P., Huckelhoven, R., Langen, G., Baltruschat, H., Stein, E., Nagarajan, S., & Kogel, K. H. (2002). Bipolaris sorokiniana, a cereal pathogen of global concern: Cytological and molecular approaches towards better control. Molecular Plant Pathology, 3(4), 185–195.

    Article  Google Scholar 

  • Kumar, A., Bhattacharya, B. K., Kumar, V., Jain, A. K., Mishra, A. K., & Chattopadhyay, C. (2016a). Epidemiology and forecasting of insect-pests and diseases for value-added agro-advisory. Mausam, 67, 267–276.

    Article  Google Scholar 

  • Kumar, S., Röder, M. S., Singh, R. P., Kumar, S., Chand, R., Joshi, A. K., & Kumar, U. (2016b). Mapping of spot blotch disease resistance using NDVI as a substitute to visual observation in wheat (Triticum aestivum L.). Molecular Breeding, 36, 95–105. https://doi.org/10.1007/s11032-016-0515-6

    Article  Google Scholar 

  • Leng, W. F., Wang, H. G., Xu, Y., & Ma, Z. H. (2012). Preliminary study on monitoring wheat stripe rust with using UAV. Acta Phytopathologica Sinica, 42, 202–205.

    Google Scholar 

  • Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2012). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computer and Electronics in Agriculture, 83, 32–46.

    Article  Google Scholar 

  • Liu, W., Cao, X., Fan, J., Wang, Z., Yan, Z., Luo, Y., West, J. S., Xu, X., & Zhou, Y. (2018). Detecting wheat powdery mildew and predicting grain yield using unmanned aerial photography. Plant Disease, 102, 1981–1988. https://doi.org/10.1094/pdis-12-17-1893-re

    Article  Google Scholar 

  • Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plümer, L., Steiner, U., & Oerke, E. C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing and Environment, 128, 21–30.

    Article  Google Scholar 

  • Nagarajan, S., Seibold, G., Kranz, J., Saari, E. E., & Joshi, L. M. (1984). Monitoring wheat rust epidemics with the Landsat-2 satellite. Phytopathology, 74, 585–587.

    Article  Google Scholar 

  • Pandey, S. P., Kumar, S., Kumar, U., Chand, R., & Joshi, A. K. (2005). Sources of inoculum and reappearance of spot blotch of wheat in rice-wheat cropping system in eastern India. European Journal of Plant Pathology, 111, 47–55.

    Article  Google Scholar 

  • Pu, R. (2017). Hyperspectral Remote Sensing (p. 490). CRC Press.

    Book  Google Scholar 

  • Rees, R. G., & Platz, G. J. (1983). Effects of yellow spot on wheat: Comparison of epidemics at different stages of crop development. Australian Journal of Agricultural Research, 34, 39–46.

    Article  Google Scholar 

  • Rosyara, U. R., Sharma, R. C., & Duveiller, E. (2006). Variation of canopy temperature depression and chlorophyll content in spring wheat genotypes and association with foliar blight resistance. Journal of Plant Breeding, Group, 1, 45–52.

    Google Scholar 

  • Rosyara, U. R., Vromman, D., & Duveiller, E. (2008). Canopy temperature depression as an indicator of correlative measure of spot blotch resistance and heat stress tolerance in spring wheat. Journal of Plant Pathology, 90, 103–107.

    Google Scholar 

  • Saari, E. E., & Prescott, L. M. (1975). A scale for appraising the foliar intensity of wheat diseases. Plant Disease Reporter, 59, 377–380.

    Google Scholar 

  • Savitzky, A., & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627–1639. https://doi.org/10.1021/ac60214a047

    Article  Google Scholar 

  • Sharma, R. C., Duveiller, E., Ahmed, F., Arun, B., Bhandari, D., Bhatt, M. R., Chand, R., Chaurasiya, P. C. P., Gharti, D. B., Hossain, M. H., Joshi, A. K., Mahto, B. N., Malaker, P. K., Shaheed, M. A., Siddique, A. B., Singh, A. K., Singh, K. P., Singh, R. N., & Singh, S. P. (2004). Helminthosporium leaf blight resistance and performance of wheat genotype across warm regions of South Asia. Plant Breeding, 123, 520–524.

    Article  Google Scholar 

  • Sharma-Poudyal, D., Sharma, R. C., & Duveiller, E. (2016). Control of Helminthosporium leaf blight of spring wheat using seed treatments and single foliar spray in Indo-Gangetic Plains of Nepal. Crop Protection, 88, 161–166.

    Article  Google Scholar 

  • Summy, K.R., Everitt, J.H., Escobar, D.E., Alaniz, M.A. & Davis, M.R. (1997). Use of airborne digital video imagery to monitor damage caused by two honeydew-excreting insects on cotton. In: Proceedings of the 16th Biennial Workshop on Color Photography and Videography in Resource Assessment, 29 Apr 01 May 1997, Weslaco, TX, pp. 238- 244

  • Terentev, A., Dolzhenko, V., Fedotov, A., & Eremenko, D. (2022). Current state of hyperspectral remote sensing for early plant disease detection: A review. Sensors, 22, 757. https://doi.org/10.3390/s22030757

    Article  Google Scholar 

  • Thenkabail, P.S., Smith, R.B. & DePauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158-182.

    Article  Google Scholar 

  • Thenkabail, P. S., Enclona, E. A., & Ashton, M. S. (2004). Accuracy assessment of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, 354–376.

    Article  Google Scholar 

  • Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperspectral data. Remote Sensing of Environment, 66(1), 41–51.

    Article  Google Scholar 

  • Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14, 1563–1575.

    Article  Google Scholar 

  • West, J. S., Bravo, C., Oberti, R., Lemaire, D., Moshou, D., & McCartney, H. A. (2003). The potential of optical canopy measurement for targeted control of field crop disease. Annual Reviews of Phytopathology, 41, 593–614.

    Article  Google Scholar 

  • Yadav, S., & Dutta, S. (2019). A study of pesticide consumption pattern and farmer’s perceptions towards pesticides: A case of Tijara Tehsil, Alwar (Rajasthan). International Journal of Current Microbiology and Applied Sciences, 8, 96–104.

    Article  Google Scholar 

  • 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, 1195–1211. https://doi.org/10.3389/fpls.2018.01195

    Article  Google Scholar 

  • Zhang, J. C., Pu, R. L., Wang, J. H., Huang, W. J., Yuan, L., & Luo, J. H. (2012). Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Computer and Electronics in Agriculture, 85, 13–23.

    Article  Google Scholar 

  • Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., & Zhao, C. (2020). A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sensing, 12, 3188. https://doi.org/10.3390/rs12193188

    Article  Google Scholar 

  • Zheng, Q., Huang, W., Cui, X., Shi, Y., & Liu, L. (2018). New spectral index for detecting wheat yellow rust using Sentinel-2 multispectral imagery. Sensors, 18, 868–886. https://doi.org/10.3390/s18030868

    Article  Google Scholar 

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Acknowledgements

The authors are very grateful to the Directors of the concerned three institutions for providing all facilities to conduct the present study. Funding received from the Indian Space Research Organization by the ICAR-National Research Centre for Integrated Pest Management for conduct of the study is duly acknowledged.

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Authors and Affiliations

  1. Department of Plant Pathology, Uttar Banga Krishi Viswavidyalaya, Pundibari, Coochbehar, 736165, India

    Nivedita Chattopadhyay & Ayon Roy

  2. ICAR- National Research Centre for Integrated Pest Management, New Delhi, 110012, India

    Karunesh K. Shukla, Ajanta Birah, Mukesh K. Khokhar & A. K. Kanojia

  3. Space Applications Centre, ISRO, Ahmedabad, 380015, India

    Rahul Nigam & Bimal K. Bhattacharya

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  1. Nivedita Chattopadhyay
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  2. Karunesh K. Shukla
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Chattopadhyay, N., Shukla, K.K., Birah, A. et al. Identification of Spectral Bands to Discriminate Wheat Spot Blotch using in Situ Hyperspectral Data. J Indian Soc Remote Sens 51, 917–934 (2023). https://doi.org/10.1007/s12524-023-01673-5

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