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Nigam, Rahul
- Assured Solar Energy Hot-Spots over Indian Landmass Detected through Remote Sensing Observations from Geostationary Meteorological Satellite
Abstract Views :258 |
PDF Views:93
Authors
Affiliations
1 Earth Ocean Atmosphere Planetary Sciences and Applications Area, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
1 Earth Ocean Atmosphere Planetary Sciences and Applications Area, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
Source
Current Science, Vol 111, No 5 (2016), Pagination: 836-842Abstract
Quantification of assured solar energy potential is essential to select locations for solar photovoltaic, thermal power plants and to quantify solar power potential. The use of remote sensing observations from geostationary satellite sensors is ideal to capture space-time variability of surface insolation. The annual clear solar energy exposure over India was determined using three years' insolation data at 8 km spatial resolution from Kalpana-1 satellite. High density solar energy pockets were diagnosed in western, central and southern India including Gujarat, Rajasthan, Madhya Pradesh, Karnataka, Tamil Nadu and Chhattisgarh states with annual solar energy exposure ranging from 2500 to 3500 kW h m-2 yr-1.Keywords
Geostationary Satellite, Renewable Energy, Remote Sensing.- Crop Type Discrimination and Health Assessment using Hyperspectral Imaging
Abstract Views :234 |
PDF Views:88
Authors
Rahul Nigam
1,
Rojalin Tripathy
1,
Sujay Dutta
1,
Nita Bhagia
1,
Rohit Nagori
1,
K. Chandrasekar
2,
Rajsi Kot
3,
Bimal K. Bhattacharya
1,
Susan Ustin
4
Affiliations
1 Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
3 M.G. Science Institute, Ahmedabad 380 009, IN
4 Environmental and Resource Sciences, University of California, Davis, CA 95616, US
1 Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
3 M.G. Science Institute, Ahmedabad 380 009, IN
4 Environmental and Resource Sciences, University of California, Davis, CA 95616, US
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1108-1123Abstract
Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%–94% for AVIRI-NG and 42%–55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (Cab) using AVIRIS-NG narrow bands. The retrieved LAI and Cab showed 19%–27% and 23%–29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662–702 and 2155–2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.Keywords
Assessment, Biotic and Abiotic Stress, Crop Classification, Health, Hyperspectral Imaging.References
- Thenkabail, P. S., Enclona, E. A., Ashton, M. S. and Van Der Meer, V., Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing Environ., 2004, 91, 354–376.
- Meerdink, S. K. et al., Linking seasonal foliar traits to VSWIRTIR spectroscopy across California ecosystems. Remote Sensing Environ., 2016, 186, 322–338.
- Gitelson, A., In Hyperspectral Remote Sensing of Vegetation (eds Thenkabail, P. S., Lyon, G. J. and Huete, A.), CRC Press-Taylor and Francis Group, Boca Raton, FL, USA, 2011, pp. 141–166.
- Ozdogan, M. and Woodcock, C. E., Resolution dependent error in remote sensing of cultivated areas. Remote Sensing Environ., 2006, 103, 203–217.
- Asner, G. P. and Martin, R. E., Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sensing Environ., 2008, 112, 3958–3970.
- Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and Jaggard, K., Extraction of vegetation biophysical parameters by Inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing Environ., 1995, 52, 63–172.
- Zarco-Tajada, P. J., Guillin-Climent, M. L., Hernandez-Clemente, R. and Catalina, A., Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imaging acquired from an unmanned aerial vehicle (UAV). Agric. For. Met., 2013, 17, 281–294.
- Green, R. O. et al., Imaging spectroscopy and the airborne visible/ infrared imaging spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65, 227–248.
- Turner, D. P., Ollinger, S., Smith, M.-L., Krankina, O. and Gregory, M., Scaling net primary production to a MODIS footprint in support of Earth observing system product validation. Int. J. Remote Sensing, 2004, 25, 1961–1979.
- Bremner, J. M., Determination of nitrogen in soil using Kjeldahl method. J. Agric. Sci., 1960, 55, 11–38.
- Aguilar, M. A., Saldaña, M. M. and Aguilar, F. J., GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. Int. J. Remote Sensing, 2012, 34, 2583–2606.
- Yonezawa, C., Maximum likelihood classification combined with spectral angle mapper algorithm for high resolution satellite imagery. Int. J. Remote Sensing, 2007, 28, 3729–3737.
- Paola, J. D. and Schowengerdt, R. A., A review and analysis of backpropagationneural networks for classification of remotely sensed multi-spectral imagery. Int. J. Remote Sensing, 1995, 16, 3033–3058.
- Vapnik, V. N., The Nature of Statistical Learning Theory, Springer Verlag, New York, USA, 1995.
- Melgani, F. and Bruzzone, L., Classification of hyperspectral remote sensing images with support vector machine. IEEE Trans. Geosci. Remote Sensing, 2004, 8, 1778–1790.
- Krebel, U., Pairwise classification and support vector machines. In Advances in Kernel Methods-Support Vector Learning (eds Schӧlkopf, B., Burges, C. J. C. and Smola, A. J.), The MIT Press, Cambridge, MA, USA, 1999, pp. 255–268.
- Green, A. A., Berman, M., Switzer, P. and Craig, M. D., A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sensing, 1988, 26, 65–74.
- Singh, A. and Harrison, A., Standardized principal components. Int. J. Remote Sensing, 1985, 6, 883–896.
- Friedl, M. A. and Brodley, C. E., Decision tree classification of land cover from remotely sensed data. Remote Sensing Environ., 1997, 61, 399–409.
- Clark, R. N. et al., Imaging spectroscopy: earth and planetary remote sensing with the USGS tetracorder and expert systems. J. Geophys. Res., 2003, 108, 5131; doi:10.1029/2002JE001847.
- Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., Deep learningbased classification of hyperspectral data. IEEE J. Appl. Earth Obs. Remote Sensing, 2014, 6, 2094–2107.
- Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A., Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sensing Lett., 2017, doi:10.1109/LGRS.
- Jacquemoud, S. et al., PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sensing Environ., 2009, 113, S56–S66.
- Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A. J. and Xiao, Q. N., A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon. Weather Rev., 2004, 132, 897–914.
- Rouse, J. W., Has, R. H., Schell, J. A., Deering, D. W. and Harlan, J. C., Monitoring the vernal advancement of retrodegradation of natural vegetation, NASA/GSFC, Type III, Final report, Greenbelt, MD, 1974, p. 371; Rouse, J. W., Haas, R. S., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings, 3rd Earth Resources Technology Satellite Symposium, 1973, vol. 1, pp. 48–623.
- Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field, C. B., Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sensing Environ., 1994, 48, 135–146.
- Hardisky, M. A., Klemas, V. and Smart, R. M., The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogramm. Eng. Remote Sensing, 1983, 49, 77–83.
- Gao, B.-C., NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environ., 1996, 58, 257–266.
- Chen, D., Huang, J. and Jackson, T. T., Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing Environ., 2005, 98, 225–236.
- Ashourloo, D., Mobasheri, M. R. and Huete, A., Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing, 2014, 6, 4723–4740.
- Bolstad, P. V. and Lillesand, T. M., Semi-automated training approaches for spectral class definition. Int. J. Remote Sensing, 1991, 13, 3157–3166.
- Clark, M. L., Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping. Remote Sensing Environ., 2017, 200, 311–325.
- Gitelson, A. A., Kaufman, Y. J., Stark, R. and Rundquist, D., Novel algorithm for remote estimation of vegetation fraction. Remote Sensing Environ., 2002, 80, 76–87.
- Gamon, J. A., Penuelas, J. and Field, C. B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing Environ., 1992, 41, 35–44.
- Serrano, L., Penuelas, J. and Ustin, S. L., Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing Environ., 2002, 81, 355–364.
- Gitelson, A. A., Zur, Y., Chivkunova, O. B. and Merzlyak, M. N., Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol., 2002, 75, 272–281.
- Birth, G. S. and McVey, G. R., Measuring colour of growing turf with a reflectance spectrometer. Agron. J., 1968, 60, 640–649.
- Huete, A. R., Liu, H., Batchily, K. and van Leeuwen, W., A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing Environ., 1997, 59, 440–451.
- Kaufman, Y. J. and Tanre, D., Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sensing Environ., 1996, 55, 65–79.
- Sims, D. A. and Gamon, J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ., 2002, 81, 337–354.
- Vogelmann, J. E., Rock, B. N. and Moss, D. M., Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sensing, 1993, 14, 1563–1575.
- Curran, P. J., Windham, W. R. and Gholz, H. L., Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. Tree Physiol., 1993, 15, 203–206.
- Sandholt, I., Rasmussen, K. and Anderson, J., A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status. Remote Sensing Environ., 2002, 79, 213–224.
- Mirik, M., Michels, G. J., Kassymzhanova-Mirik, S. and Elliott, N. C., Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Comp. Electron. Agric., 2007, 57, 123–134.
- Xu X., Li, J., Wu, C. and Plaza, A., Regional clustering-based spatial preprocessing for hyperspectral unmixing. Remote Sensing Environ., 2018, 204, 333–346.
- Detection of rice leaf folder, Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Crambidae) infestation using ground-based hyperspectral radiometry
Abstract Views :123 |
PDF Views:71
Authors
Bhubanananda Adhikari
1,
Radhakrushna Senapati
2,
Minati Mohapatra
3,
Laxminarayan Mohapatra
3,
Rahul Nigam
4,
Shyamaranjan Das Mohapatra
2
Affiliations
1 ICAR-National Rice Research Institute, Cuttack 753 006, India; Odisha University of Agriculture and Technology, Bhubaneswar 751 003, India, IN
2 ICAR-National Rice Research Institute, Cuttack 753 006, India, IN
3 Odisha University of Agriculture and Technology, Bhubaneswar 751 003, India, IN
4 Space Application Centre, Indian Space Research Organisation, Ahmedabad 380 015, India, IN
1 ICAR-National Rice Research Institute, Cuttack 753 006, India; Odisha University of Agriculture and Technology, Bhubaneswar 751 003, India, IN
2 ICAR-National Rice Research Institute, Cuttack 753 006, India, IN
3 Odisha University of Agriculture and Technology, Bhubaneswar 751 003, India, IN
4 Space Application Centre, Indian Space Research Organisation, Ahmedabad 380 015, India, IN
Source
Current Science, Vol 124, No 8 (2023), Pagination: 964-975Abstract
Hyperspectral remote sensing is a useful technique for detecting spatio-temporal changes in crop morphological and physiological health. In order to identify the pest-sensitive bands for rice leaf folder (RLF), the ground-based hyperspectral data were recorded at varying damage levels. The first- and second-order derivatives were correlated with correlation coefficient r and per cent leaf damage. The common region identified were recognized as sensitive regions (508–529, 671–680, 721–759, 779–786 and 804–820 nm). The absorption dips were also found using Sensitivity and Continuum Removal Analysis. Combining all, a total of nine spectral bands (518, 549, 661, 674, 678, 731, 789, 816 and 898 nm) were identified as pest-sensitive bands for RLF. The feature-selection method was employed using RELIEFF algorithm to find out the band combinations and bands 518, 661 and 731 nm yielded maximum accuracy of 81.67%Keywords
Hyperspectral sensing, rice leaf folder, sen-sitive spectral bands, spectroradiometer.References
- Mohapatra, S. D. et al., Current status and future prospects in biotic stress management in rice. Oryza, 2021, 58, 168–193.
- Martinelli, F. et al., Advanced methods of plant disease detection. A review. Agron. Sustain. Dev., 2015, 35, 1–25.
- Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plumer, L., Steiner, U. and Oerke, E. C., Development of spectral indices for detecting and identifying plant diseases. Remote Sensing Environ., 2013, 128, 21–30.
- Huang, J. R., Liao, H. J., Zhu, Y. B., Sun, J. Y., Sun, Q. H. and Liu, X. D., Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis). Comput. Electron. Agric., 2012, 82, 100–107.
- Calderon, R., Navas-Cortes, J. A., Lucena, C. and Zarco-Tejada, P. J., High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing En-viron., 2013, 139, 231–245.
- Ghyar, B. S. and Birajdar, G. K., Computer vision-based approach to detect rice leaf diseases using texture and color descriptors. In Proceedings of the International Conference on Inventive Computing and Informatics, Coimbatore, 23–24 November 2017.
- Nigam, R. et al., Ground-based hyperspectral remote sensing to discriminate biotic stress in cotton crop. In Multispectral, Hyper-spectral and Ultraspectral Remote Sensing Technology, Techniques and Applications VI, SPIE, 2016, vol. 9880, pp. 89–98.
- Huang, W. J. et al., New optimized spectral indices for identifying and monitoring winter wheat diseases. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2014, 7, 2516–2524.
- Mohapatra, S. D. et al., Eco-smart pest management in rice farming: prospects and challenges. Oryza, 2019, 56, 143–155.
- Dash, L., Ramalakshmi, V., Padhy, D. and Tripathy, B., Breeding for resistance against leaf folder in rice. Indian J. Pure Appl. Biosci., 2020, 8(6), 248–253.
- Adhikari, B., Mohapatra, L. N., Senapati, R., Mohapatra, M., Muduli, L. and Mohapatra, S. D., Biochemical changes in rice leaves due to rice leaf folder Cnaphalocrocis medinalis (Guenee) infestation. Pharma Innov., 2022, SP-11(8), 1463–1468.
- Tanwar, R. K., Singh, S., Singh, S. P., Kanwar, V. K., Kumar, R., Khokar, M. K. and Mohapatra, S. D., Implementing the systems approach in rice pest management: India context. Oryza, 2019, 56, 136–142.
- Litsinger, J. A., Bandong, J. P., Canapi, B. L., dela Cruz, C. G., Pantua, P. C., Alviola., A. L. and Batay-An, E. H., Evaluation of action thresholds for chronic rice insect pests in the Philippines. Int. J. Pest Manage., 2006, 52, 181–194.
- Jin, J. and Wang, Q., Evaluation of informative bands used in different PLS regressions for estimating leaf biochemical contents from hyperspectral reflectance. Remote Sensing, 2019, 11(2), 197.
- Xu, B., Li, X., Hou, W., Wang, Y. and Wei, Y., A similarity-based ranking method for hyperspectral band selection. IEEE Trans. Geosci. Remote Sensing, 2021, 59(11), 9585–9599.
- Saranya, G. and Pravin, A., Feature selection techniques for disease diagnosis system: a survey. In Artificial Intelligence Techniques for Advanced Computing Applications, Springer, Singapore, 2021, pp. 249–258.
- Ashrith, K. N., Sreenivas, A. G., Guruprasad, G. S., Patil, N. B., Hanchinal, S. G. and Chavan, I., Influence of weather parameters on the occurrence of major insect pests in conventional rice ecosystem. Oryza, 2017, 54(3), 324–329.
- IRRI, Standard Evaluation System (SES) for Rice, 5th edn, International Rice Research Institute, Manila, 2013.
- Chintalapati, P., Javvaji, S. and Gururaj, K., Measurement of damaged leaf area caused by leaffolder in rice. J. Entomol. Zool Stud., 2017, 5, 415–417.
- Salisbury, J. W., Spectral measurements field guide. Defense Intelligence Agency Central Measurement and Signature Intelligence (MASINT) Office, Report No. ADA362372, Fort Belvoir, 1998, pp. 1–82.
- Zhao, J., Dongyan, Z., Juhua, L., Yingying, D., Hao, Y. and Wen-jiang, H., Characterization of the rice canopy infested with brown spot disease using field hyperspectral data. Wuhan Univ. J. Nat. Sci., 2012, 17, 086–092.
- Curran, P. J., Dungan, J. L., Macler, B. A., Plummer, S. E. and Peterson, D. L., Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration. Remote Sensing Environ., 1992, 39, 153–166.
- Elvidge, C. D. and Chen, Z., Comparison of broad-band and narrowband red and near-infrared vegetation indices. Remote Sensing Environ., 1995, 54, 38–48.
- Bao, J., Chi, M. and Benediktsson, J. A., Spectral derivative features for classification of hyperspectral remote sensing images: experimental evaluation. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 2013, 6(2), 594–601.
- Thorp, K. R., Wang, G., Bronson, K. F., Badaruddin, M. and Mon J., Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield. Comput. Electron. Agric., 2017, 136, 1–12.
- Tsai, F. and Philport, W., Derivative analysis of hyperspectral data. Remote Sensing Environ., 1998, 66, 41–51.
- Clevers, J. G. P. W., Kooistra, L. and Salas, E. A. L., Study of heavy metal contamination in river floodplains using the rededge position in spectroscopic data. Int. J. Remote Sensing, 2004, 25, 3883–3895.
- Smith, K. L., Steven, M. D. and Colls, J. J., Use of hyperspectral derivative ratios in the red edge region to identify plant stress responses to gas leak. Remote Sensing Environ., 2004, 92, 207–217.
- le Maire, G., Francois, C. and Dufrene, E., Towards universal broad leaf chlorophyll indices using PROSPECT simulated data-base and hyperspectral reflectance measurements. Remote Sensing Environ., 2004, 89(1), 1–28.
- Mageshwaran, M., Srinivasan, M. R. and Sivasamy, R., Detection and estimation of damage caused by rice yellow stem borer Scirpophaga incertulas (Walker) by hyperspectral radiometry. In Proceedings of the National Symposium on Emerging Trends in Ecofriendly Insect Pest Management (eds Srinivasan, M. R. et al.), 2014, pp. 438–440.
- Tan, Y., Sun, J., Zhang, B., Chen, M., Liu, Y. and Liu, X., Sensitivity of a ratio vegetation index derived from hyperspectral remote sensing to the brown planthopper stress on rice plants. Sensors, 2019, 19, 375.
- Zhao, J. L., Zhao, C. J., Yang, H., Zhang, D. Y., Dong, Y. Y. and Yuan, L., Identification and characterization of spectral response properties of rice canopy infested by leaf folder. Int. J. Agric. Biol., 2013, 15, 694–700.
- Carter, G. A., Seal, M. R. and Haley, T., Airborne detection of southern pine beetle damage using key spectral bands. Can. J. For. Res., 1998, 28, 1040–1045.
- Kokaly, R. F. and Clark, R. N., Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise linear regression. Remote Sensing Environ., 1998, 67, 267–287.
- Huang, J. R., Sun, J. Y., Liao, H. J. and Liu, X. D., Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer. Precis. Agric., 2015, 16, 148–163.
- Ren, J., Wang, R., Liu, G., Feng, R., Wang, Y. and Wu, W., Partitioned reliefF method for dimensionality reduction of hyperspec-tral images. Remote Sensing, 2020, 12(7), 1104.
- Pal, M. and Foody, G. M., Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sensing, 2010, 48(5), 2297–2307.
- Rezaei, Y., Mobasheri, M. R. and Zoej, M. V., Unsupervised information extraction using absorption line in hyperion images. Int. Arch. Photogramm, Remote Sensing Spat. Inf. Sci., 2008, 37, 383–388.
- Din, M., Zheng, W., Rashid, M., Wang, S. and Shi, Z., Evaluating hyperspectral vegetation indices for leaf area index estimation of Oryza sativa L. at diverse phenological stages. Front. Plant Sci., 2017, 8, 820.
- Gu, X., Cai, W., Fan, Y., Ma, Y., Zhao, X. and Zhang, C., Estimating foliar anthocyanin content of purple corn via hyperspectral model. Food Sci. Nutr., 2018, 6(3), 572–578.
- Liang, G. C., Ouyang, Y. C. and Dai, S. M., Detection and classification of rice infestation with rice leaf folder (Cnaphalocrocis medinalis) using hyperspectral imaging techniques. Remote Sensing, 2021, 13(22), 4587.
- Fan, Y., Wang, T., Qiu, Z., Peng, J., Zhang, C. and He, Y., Fast detection of striped stem borer (Chilo suppressalis Walker) infested rice seedling based on visible/near-infrared hyperspectral imaging system. Sensors, 2017, 17, 2470.
- Liu, T., Shi, T., Zhang, H. and Wu, C., Detection of rise damage by leaf folder (Cnaphalocrocis medinalis) using unmanned aerial vehicle based hyperspectral data. Sustainability, MDPI, Open Access Journal, 2020, 12(22), 1–14.
- Adhikari, B. et al., Discrimination of healthy and damaged rice by leaf folder using hyperspectral Sensing. In First Indian Rice Congress, ICAR-National Rice Research Institute, Cuttack, 2020, pp. 681–684.
- Han, L., Estimating chlorophyll-a concentration using first‐derivative spectra in coastal water. Int. J. Remote Sensing, 2005, 26(23), 5235–5244.
- Thenkabail, P. S., Enclona, E. A., Asthon, M. S. and Van der, M. B., Accuracy assessment of hyperspectral wave band performance for vegetation analysis applications. Remote Sensing Environ., 2004, 91, 354–376.
- Ranjitha, G., Srinivasan, M. R. and Rajesh, A., Detection and estimation of damage caused by thrips Thrips tabaci (Lind) of cotton using hyperspectral radiometer. Agrotechnology, 2014, 3(1), 123–128.
- Prasannakumar, N. R., Chander, S. and Sahoo, R. N., Characterization of brown planthopper damage on rice crops through hyperspectral remote sensing under field conditions. Phytoparasitica, 2014, 42, 387–395.
- Xue, L. Z., Xu, L., Tan, Y. and Liu, X. D., Spectral characteristics of different rice cultivars damaged by the brown planthopper Nilaparvata lugens. J. Nanjing Agric. Univ., 2015, 38, 796–803.
- Liu, Z. Y., Huang, J. F. and Tao, R. X., Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Sci., 2008, 15(3), 232–242.
- Naik, B. B., Naveen, H. R., Sreenivas, G., Choudary, K. K., Dev-kumar, D. and Adinarayana, J., Identification of water and nitrogen stress indicative spectral bands using hyperspectral remote sensing in maize during post-monsoon season. J. Indian Soc. Remote Sensing, 2020, 48(12), 787–795.
- Singh, B., Singh, M., Suri, K., Sharma, A. and Mishra, P. K., Use of hyper-spectral data for detection of rice leaf folder infestation. J. Res. Punjab Agric. Univ., 2013, 50(3 and 4), 147–150.
- Wu, B., Chen, C., Kechadi, T. M. and Sun, L., A comparative evaluation of filter-based feature selection methods for hyper-spectral band selection. Int. J. Remote Sensing, 2013, 34(22), 7974–7990.