Selection of sensitive bands for assessing Alernaria blight diseased severity grades in mustard crops using hyperspectral reflectance
DOI:
https://doi.org/10.54386/jam.v25i2.2057Keywords:
hyperspectral, spectroscopic, spectral band, derivarive, reflectanceAbstract
Recent development in remote sensing technology using hyperspectral reflectance or spectroscopic data was enabled the rapid and ongoing progression of monitoring, mapping, and surveillance/detection of insects tools for better crop management. This study describes a spectroscopic based methodology to escalation the efficiency of present surveillance practices (insect traps and human examinations) for detection pest infestation (e.g., Alternaria blight in mustard crop). The methodology uses ground based hyperspectral data across the spectral bands 350-2500nm at 1 nm interval. Three different statistical procedures such as correlation (between reflectance, 1st and 2nd derivatives with diseased severity grades), continuum removal analysis was implemented for selection of sensitive bands. In this method, we explore the combinations of different selected sensitive spectral bands and regions to separate diseased crops. The objectives of this research is to develop a novel methodology for selection of sensitive bands to Alternaria blight diseased crops. The development of such methodology would provide researchers, Agronomist, and remote sensing practitioners reliable and stable method to achieve faster technique with higher accuracy to mapping of Alternaria blight diseased crops.
References
Bhat, H. A., Ahmad, K., Ahanger, R. A., Qazi, N. A., Dar, N. A., and Ganie, S. A. (2013). Status and symptomatology of Alternaria leaf blight (Alternaria alternata) of Gerbera (Gerbera jamisonii) in Kashmir valley. Afr. J. Agr. Res., 8(9): 819-823. DOI: 10.5897/AJAR12.1766
Chattopadhyay, C., Kolte, and S.J., Waliyar, F. (2015). Diseases of Edible Oilseed Crops. CRC Press, Boca Raton, Florida, USA, xxvii + 456 p.
Curran, P.J. (1989). Remote sensing of foliar chemistry. Remote Sens. Environ., 30: 271–278, DOI: 10.1016/0034-4257(89)90069-2
Filella, I., and Peñuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens., 15: 1459-1470. DOI: 10.1080/01431169408954177
Ge, Y., Atefi, A., Zhang, H., Miao, C., Ramamurthy, R. K., and Sigmon, B. (2019). High-throughput analysis of leaf physiological and chemical traits with VISNIR-SWIR spectroscopy: a case study with a maize diversity panel. Plant Methods, 15: 1–12. DOI:10.1186/s13007-019-0450-8
Gholizadeh, A., and Kopackova, V. (2019). Detecting vegetation stress as a soil contamination proxy-a review of optical proximal and remote sensing techniques. Int. J. Environ. Sci. Technol., 16: 2511–2524. DOI:10.1007/s13762-019-02310-w
Giblot-Ducray, D., Correll, R., Collins, C., Nankivell, A., Downs, A., Pearce, I., Mckay, A.C., and Ophel-Keller, K.M. (2016). Detection of grape phylloxera (Daktulosphaira vitifoliae Fitch) by real-time quantitative PCR: Development of a soil sampling protocol. Aust. J. Grape Wine Res., 22: 469–477. DOI: https://doi.org/10.1111/ajgw.12237
GOI (Government of India), (2020). Pocket Book of Agricultural Statistics, Directorate of Economics and Statistics, Department of Agriculture, Cooperation and Farmers’ Welfare, Ministry of Agriculture and Farmers’ Welfare, 120 p, downloaded from https://eands.dacnet.nic.in/PDF/Pocket%202020-%20Final%20web%20file.pdf on 14 Mar 2022, 1945 hrs IST.
Horler, D. N. H., Dockray, M., and Barber, J. (1983). The red-edge of plant leaf reflectance. Int. J. Remote Sens., 4: 273–288. DOI: https: 10.1080/01431168308948546
Kobayashi, T., Kanda, E., Kitada, K., Ishiguro, K., and Torigoe, Y. (2001). Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathol., 91(3): 316–323. DOI: 10.1094/PHYTO.2001.91.3.316.
Kobayashi, T., Kanda, E., Naito, S., Nakajima, T., Arakawa, I., Nemoto, K., Honma, M., Toujyou, H., Ishiguro, K., Kitada, K., and Torigoe, Y. (2003). Ratio of rice reflectance for estimating leaf blast severity with a multispectral radiometer. J. Gen. Plant Pathol., 69(1): 17-22. DOI: 10.1007/s10327-002-0006-y
Kokaly, R. F., and Clark, R. N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens. Environ. 67: 267–287. DOI: 10.1016/S0034-4257(98)00084-4
Kumar, L., Schmidt, K., Dury, S., and Skidmore, A. (2001) Imaging Spectrometry and Vegetation Science. In: van der Meer, F.D. and de Jong, S.M. (eds.), Imaging Spectrometry: Basic Principles and Prospective Applications, Dordrecht: Kluwer Academic Publishers., 111-155
Liu, Z. Y., Huang, J.F., Tao, R.X., and Zhang, H.Z. (2008). Estimating rice brown spot disease severity based on principal component analysis and radial basis function neural network. Spectros. Spectral Analysis, 28(9): 2156–2160.
Mallick, S., Kumari, Pranita., Gupta. Moni., and Gupta, Sachin. (2015). Effect of Alternaria-blight infection on biochemical parameters, quantity and quality of oil of mustard genotypes. Indian J. Plant Physiol., 20(4): 310-316. DOI: 10.1007/s40502-015-0178-z.
Nigam, Rahul, Tripathy, Rojalin., Dutta, Sujay, Bhagia, Nita., Nagori, Rohit., Chandrasekar, K., Kot, Rajsi, Bhattacharya, Bimal K., and Ustin, Susan. (2019). Crop type discrimination and health assessment using hyperspectral imaging. Current Sci., 116 (7), 1108-1123. DOI: 10.18520/cs/v116/i7/1108-1123
Nutter Jr, F.W., Guan, J., Gotlieb, A.R., Rhodes, L.H., Grau, C.R. and Sulc, R.M. (2002). Quantifying alfalfa yield losses caused by foliar diseases in Iowa, Ohio, Wisconsin, and Vermont. Plant Dis., 86(3): 269-277. DOI: 10.1094/PDIS.2002.86.3.269
Pearson, K., and Lee, A. (1902). On the laws of inheritance in man. I. Inheritance of physical characteristics. Biometrika, 2: 357-372
Raikes, C., and Burpee, L. L. (1998). Use of multispectral radiometry for assessment of Rhizoctonia blight in creeping bentgrass. Phytopathol., 88: 446–449, DOI:10.1094/PHYTO.1998.88.5.446
San Bautista, A., Fita, D., Franch, B., Castiñeira-Ibanez, S., Arizo, P., Sanchez-Torres, M.J., Becker-Reshef, I., Uris, A., and Rubio, C. (2022). Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine. Agron., 12: 708. DOI:10.3390/agronomy1203070
Savitzky, A., and Golay, M.J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8): 1627-1639. DOI: https://doi.org/10.1021/ac60214a047
Tan, SY. (2016). Developments in Hyperspectral Sensing. In: Pelton, J., Madry, S., Camacho-Lara, S. (Eds.). Handbook of Satellite Applications. Springer, New York, NY. DOI:10.1007/978-1-4614-6423-5_101-1
Yadav, M.S. Amrender Kumar, C. Chattopadhyay, and D.K. Yadava. (2022). Epidemiological models based on meteorological variables to forewarn Alternaria blight of rapeseed-mustard. J. Agrometeorol., 24(1):55–59. https://doi.org/10.54386/jam.v24i1.782
Zhang, J., Wang, N., Yuan, L., Chen, F N., and Wu, K.H. (2017). Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements. Biosyst. Eng., 162: 20-29, DOI: 10.1016/j.biosystemseng.2017.07.003
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 KARUNESH K. SHUKLA, AJANTA BIRAH, RAHUL NIGAM, A. K. KANOJIA, MUKESH KM KHOKHAR, B. K. BHATTACHARYA, SUBHASH CHANDER
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is a human-readable summary of (and not a substitute for) the license. Disclaimer.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
