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Effect of temperature on brown planthopper Infestation in rice using hyperspectral remote Sensing


Affiliations
1 Tamil Nadu Agricultural University, Coimbatore 641 003, India., India
2 Indian Farmers Fertilizers Cooperative Limited, Coimbatore 641 003, India., India
 

Hyperspectral remote sensing captures images in multiple wavelengths and is widely used to detect plant stress in agriculture. A study was conducted on brown planthopper (BPH) infestation in rice at various temperature regimes (15°C, 20°C, 25°C, 30°C and 35°C). The experimentation was done in the Environmental Control Chamber, Tamil Nadu Agricultural University, Coimbatore, India. The field spectroradiometer and vegetation indices were used to study the early and late infestations of BPH in rice. The results reveal that reflectance at certain wavelengths (550, 670 and 700 nm) indicates plant stress. Among the vegetation indices, MCARI performed better than NDVI, PRI, NDRE and SR for the detection of early and late infestation of BPH. Hence, hyperspectral reflectance from rice has been used to detect pest damage and improve management policies.

Keywords

Brown planthopper, hyperspectral sensor, Plant stress, rice, vegetation indices.
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  • Oghaz, M. M. D., Razaak, M., Kerdegari, H., Argyriou, V. and Remagnino, P., Scene and environment monitoring using aerial im-agery and deep learning. IEEE, 2019.
  • Childs, N. and LeBeau, B., Rice Outlook, Report, FAO, 2022.
  • Saravanakumar, V., Lohano, H. D. and Balasubramanian, R., A dis-trict-level analysis for measuring the effects of climate change on production of rice: evidence from southern India. Theor. Appl. Cli-matol., 2022, 150(3–4), 941–953.
  • Min, S., Lee, S. W., Choi, B.-R., Lee, S. H. and Kwon, D. H., In-secticide resistance monitoring and correlation analysis to select appropriate insecticides against Nilaparvata lugens (Stål), a migra-tory pest in Korea. J. Asia-Pac. Entomol., 2014, 17(4), 711–716.
  • Cabauatan, P. Q., Cabunagan, R. C. and Choi, I.-R., Rice viruses transmitted by the brown planthopper Nilaparvata lugens. In Planthoppers: New Threats to the Sustainability of Intensive Rice Production Systems in Asia, IRRI Books, International Rice Res-earch Institute, 2009, pp. 357–368.
  • Mohapatra, S. D. et al., Eco-smart pest management in rice farming: prospects and challenges. Oryza, 2019, 56(Special Issue), 143–155.
  • Muller, A., Prakash, A., Lazutkaite, E. M. D., Amdihun, A. and Ouma, J., Scientific linkages between climate change and (trans-boundary) crop pest and disease outbreaks. In TMG Working Paper, 2022, p. 29.
  • Abd El-Ghany, N. M., Abd El-Aziz, S. E. and Marei, S. S., A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environ. Sci. Pollut. Res., 2020, 27, 33503–33515.
  • Wang, F. M., Huang, J. F. and Wang, X. Z., Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol., 2008, 50(3), 291–299.
  • Katsoulas, N., Elvanidi, A., Ferentinos, K. P., Kacira, M., Bartzanas, T. and Kittas, C., Crop reflectance monitoring as a tool for water stress detection in greenhouses: a review. Biosyst. Eng., 2016, 151, 374–398.
  • Curran, P., Principles of Remote Sensing, Longman, London, UK, 1985.
  • Prasannakumar, N. R., Chander, S., Sahoo, R. N. and Gupta, V. K., Assessment of brown planthopper (Nilaparvata lugens) damage in rice using hyperspectral remote sensing. Int. J. Pest Manage., 2013, 59(3), 180–188.
  • Seager, S., Turner, E. L., Schafer, J. and Ford, E. B., Vegetation’s red edge: a possible spectroscopic biosignature of extraterrestrial plants. Astrobiology, 2005, 5(3), 372–390.
  • Yang, C. M. and Chen, R. K., Differences in growth estimation and yield prediction of rice crop using satellites data simulated from near ground hyperspectral reflectance. J. Photogramm. Remote Sensing, 2007, 12(1), 93–105.
  • Liu, X. D. and Sun, Q. H., Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method. Int. J. Pest Manage., 2016, 62(3), 205–213.
  • Liu, J., Han, J., Chen, X., Shi, L. and Zhang, L., Nondestructive de-tection of rape leaf chlorophyll level based on vis–NIR spectroscopy. Spectrochim. Acta Part A, 2019, 222, 117202.
  • Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q. and Liu, X., Hyper-spectral detection of rice damaged by rice leaf folder (Cnaphalo-crocis medinalis). Comput. Electron. Agric., 2012, 82, 100–107.
  • Abdel-Rahman, E. M., Ahmed, F. B., van den Berg, M. and Way, M. J., Potential of spectroscopic data sets for sugarcane thrips (Fulmekiola serrata Kobus) damage detection. Int. J. Remote Sens-ing, 2010, 31(15), 4199–4216.
  • Madasamy, B., Balasubramaniam, P. and Dutta, R., Microclimate-based pest and disease management through a forewarning system for sustainable cotton production. Agriculture, 2020, 10(12), 641.
  • Penuelas, J., Gamon, J. A., Griffin, K. L. and Field, C. B., Assessing community type, plant biomass, pigment composition, and photo-synthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing Environ., 1993, 46(2), 110–118.
  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., DeColstoun, E. B. and McMurtrey Iii, J. E., Estimating corn leaf chlorophyll concen-tration from leaf and canopy reflectance. Remote Sensing Environ., 2000, 74(2), 229–239.
  • Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ., 1974, 351(1), 309.
  • Gates, D. M., Keegan, H. J., Schleter, J. C. and Weidner, V. R., Spectral properties of plants. Appl. Opt., 1965, 4(1), 11–20.
  • Fitzgerald, G. J., Rodriguez, D., Christensen, L. K., Belford, R., Sadras, V. O. and Clarke, T. R., Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environ-ments. Precis. Agric., 2006, 7, 233–248.
  • Carter, G. A., Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote Sensing, 1994, 15(3), 697–703.
  • Yang, C. M., Cheng, C. H. and Chen, R. K., Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Sci., 2007, 47(1), 329–335.
  • Sahoo, R. N., Ray, S. S. and Manjunath, K. R., Hyperspectral re-mote sensing of agriculture. Curr. Sci., 2015, 108(5), 848–859.
  • Hunt Jr, E. R. and Rock, B. N., Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens-ing Environ., 1989, 30(1), 43–54.
  • Clarke, A. and Fraser, K. P. P., Why does metabolism scale with temperature? Funct. Ecol., 2004, 18(2), 243–251.
  • Taylor, R. A. J., Herms, D. A., Cardina, J. and Moore, R. H., Climate change and pest management: unanticipated consequences of trophic dislocation. Agronomy, 2018, 8(1), 7.
  • Hannigan, S., Nendel, C. and Krull, M., Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius. J. Pest Sci., 2022, 1–14.
  • Priyadarshini, S., Ghosh, S. K. and Nayak, A. K., Field screening of different chilli cultivars against important sucking pests of chilli in West Bengal. Bull. Environ., Pharmacol. Life Sci., 2019, 8(7), 134–140.
  • Yan, T., Xu, W., Lin, J., Duan, L., Gao, P., Zhang, C. and Lv, X., Com-bining multi-dimensional convolutional neural network (CNN) with visualization method for detection of Aphis gossypii Glover infec-tion in cotton leaves using hyperspectral imaging. Front. Plant Sci., 2021, 12, 604.
  • Polivova, M. and Brook, A., Detailed investigation of spectral vegeta-tion indices for fine field-scale phenotyping. Vegetation Index Dyna-mics, 2021.
  • 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.
  • Vanegas, F., Bratanov, D., Powell, K., Weiss, J. and Gonzalez, F., A novel methodology for improving plant pest surveillance in vine-yards and crops using UAV-based hyperspectral and spatial data. Sensors, 2018, 18(1), 260.
  • Pinter Jr, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T. and Upchurch, D. R., Remote sensing for crop management, 2003.
  • Broge, N. H. and Leblanc, E., Comparing prediction power and sta-bility of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2001, 76(2), 156–172.
  • de Lima, I. P., Jorge, R. G. and de Lima, J. L. M. P., Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices. Front. Remote Sensing, 2021, 2, 762093.
  • Kurbanov, R. and Zakharova, N., Justification and selection of vegetation indices to determine the early soybeans readiness for harvesting. EDP Sciences, 2021.
  • Luo, J., Huang, W., Zhao, J., Zhang, J., Zhao, C. and Ma, R., Detecting aphid density of winter wheat leaf using hyperspectral measure-ments. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2013, 6(2), 690–698.
  • Prabhakar, M., Prasad, Y., Desai, S. and Thirupathi, M., Spectral and spatial properties of rice brown plant hopper and groundnut late leaf spot disease infestation under field conditions. J. Agrome-teorol., 2013, 15, 57–62.
  • Sogawa, K., The rice brown planthopper: feeding physiology and host plant interactions. Ann. Rev. Entomol., 1982, 27(1), 49–73.
  • Watanabe, T. and Kitagawa, H., Photosynthesis and translocation of assimilates in rice plants following phloem feeding by the planthopper Nilaparvata lugens (Homoptera: Delphacidae). J. Econ. Entomol., 2000, 93(4), 1192–1198.
  • Liu, J. L., Yu, J. F., Wu, J. C., Yin, J. L. and Gu, H. N., Physiological responses to Nilaparvata lugens in susceptible and resistant rice varie-ties: allocation of assimilates between shoots and roots. J. Econ. Entomol., 2008, 101(2), 384–390.
  • Vanitha, K., Suresh, S. and Gunathilagaraj, K., Influence of brown planthopper Nilaparvava lugens feeding on nutritional biochemis-try of rice plant. ORYZA – Int. J. Rice, 2011, 48(2), 142–146.

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  • Effect of temperature on brown planthopper Infestation in rice using hyperspectral remote Sensing

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Authors

S. Sivaranjani
Tamil Nadu Agricultural University, Coimbatore 641 003, India., India
V. Geethalakshmi
Tamil Nadu Agricultural University, Coimbatore 641 003, India., India
S. Pazhanivelan
Tamil Nadu Agricultural University, Coimbatore 641 003, India., India
J. S. Kennedy
Tamil Nadu Agricultural University, Coimbatore 641 003, India., India
S. P. Ramanathan
Tamil Nadu Agricultural University, Coimbatore 641 003, India., India
R. Gowtham
Indian Farmers Fertilizers Cooperative Limited, Coimbatore 641 003, India., India
K. Pugazenthi
Tamil Nadu Agricultural University, Coimbatore 641 003, India., India

Abstract


Hyperspectral remote sensing captures images in multiple wavelengths and is widely used to detect plant stress in agriculture. A study was conducted on brown planthopper (BPH) infestation in rice at various temperature regimes (15°C, 20°C, 25°C, 30°C and 35°C). The experimentation was done in the Environmental Control Chamber, Tamil Nadu Agricultural University, Coimbatore, India. The field spectroradiometer and vegetation indices were used to study the early and late infestations of BPH in rice. The results reveal that reflectance at certain wavelengths (550, 670 and 700 nm) indicates plant stress. Among the vegetation indices, MCARI performed better than NDVI, PRI, NDRE and SR for the detection of early and late infestation of BPH. Hence, hyperspectral reflectance from rice has been used to detect pest damage and improve management policies.

Keywords


Brown planthopper, hyperspectral sensor, Plant stress, rice, vegetation indices.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi10%2F1194-1200