Abstract
The present review provides a perspective angle on the historical and cutting-edge strategies of remote sensing techniques and its applications, especially for insect pest and plant disease management. Remote sensing depends on measuring, recording, and processing the electromagnetic radiation reflected and emitted from the ground target. Remote sensing applications depend on the spectral behavior of living organisms. Today, remote sensing is used as an effective tool for the detection, forecasting, and management of insect pests and plant diseases on different fruit orchards and crops. The main objectives of these applications were to collate data that help in decision-making for insect pest management and decreasing the environmental pollution of chemical pesticides. Airborne remote sensing has been a promising and useful tool for insect pest management and weed detection. Furthermore, remote sensing using satellite information proved to be a promising tool in forecasting and monitoring the distribution of locust species. It has also been used to help farmers in the early detection of mite infestation in cotton fields using multi-spectral systems, which depend on color changes in canopy semblance over time. Remote sensing can provide fast and accurate forecasting of targeted insect pests and subsequently minimizing pest damage and the management costs.
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References
Abdullah A, Umer MZ (2004) Applications of remote sensing in pest scouting: evaluating options and exploring possibilities. Proc VII Int Conf on Precision Agriculture and Other Precision Resources Management, Hyatt Regency, Minneapolis, MN, St. Paul, MN: Precision Agriculture Center, University of Minnesota (USA), July 25–28 pp:14. https://www.researchgate.net/publication/265996671
Acharya MC, Thapa RB (2015) Remote sensing and its application in agricultural pest management. J Agric Environ 6:43–61. https://doi.org/10.3126/aej.v16i0.19839
Al-Kindi KM, Kwan P, Andrew NR, Welch M (2017) Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities. Peer J 5:e3752. https://doi.org/10.7717/peerj.3752
Andreo V (2013) Remote sensing and geographic information systems in precision farming. Technical report available: http://aulavirtual.ig.conae.gov.ar /moodle/pluginfile.php/513/mod_page/content/71/seminario_andreo_2013.pdf (Retrieved 16 April 2015) pp. 34.
Avery TE, Berlin GL (1992) Fundamentals of remote sensing and airphoto interpretation, 5th edn, Prentice Hall, p 472. https://www.flipkart.com/fundamentals-remote-sensing-airphoto-interpretation-5th-edition-5-ed/p/itmczymfk6w3jwh6
Bawden FC (1933) Infra-red photography and plant virus diseases. Nature 132:168. https://doi.org/10.1038/132168a0
Blaes X, Chomé G, Lambert M-J, Traoré PS, Schut AGT, Defourny P (2016) Quantifying fertilizer application response variability with VHR satellite NDVI time series in a rainfed smallholder cropping system of Mali. Remote Sens 8(6):531. https://doi.org/10.3390/rs8060531
Brenchley GH (1968) Aerial photography for the study of plant diseases. Annu Rev Phytopathol 6:1–20. https://doi.org/10.1146/annurev.py.06.090168000245
Brydegaard M, Jansson S (2019) Advances in entomological laser radar. The Journal of Engineering 2019:7542–7545. https://doi.org/10.1049/joe.2019.0598
Chapman JW, Smith AD, Woiwod IP, Reynolds DR, Riley JR (2002) Development of vertical-looking radar technology for monitoring insect migration. Comput Electron Agric 35(2-3):95–110. https://doi.org/10.1016/S0168-1699(02)00013-3
Chuang Y-CM, Shiu Y-S (2016) A comparative analysis of machine learning with WorldView-2 pan-sharpened imagery for tea crop mapping. Sensors 16(5):594. https://doi.org/10.3390/s16050594
Colwell RN (1956) Determining the prevalence of certain cereal diseases by means of aerial photography. Hilgardia 26(5):223–286
Coops NC, Johnson M, Wulder MA, White JC (2006) Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens Environ 103(1):67–80. https://doi.org/10.1016/j.rse.2006.03.012
Dhau I, Adam E, Mutanga O, Ayisi KK (2018a) Detecting the severity of maize streak virus infestations in maize crop using in situ hyperspectral data. TransR Soc S Afr 73(1):8–15. https://doi.org/10.1080/0035919X.2017.1370034
Dhau I, Adam E, Mutanga O, Ayisi KK, Mutanga O (2018b) Detection and mapping of maize streak virus using RapidEye satellite imagery. Geocarto Int 34:856–866. https://doi.org/10.1080/10106049.2018.1450448
Dutta S, Singh SK, Panigrahy S (2014) Assessment of late blight induced diseased potato crops: a case study for West Bengal district using temporal AWiFS and MODIS data. J Indian Soc Remote Sens 42(2):353–361. https://doi.org/10.1007/s12524-013-0325-9
Everitt JH, David E, Kenneth S, Mario A, Michael D (1996) Using spatial information technologies for detecting and mapping whitefly and harvester ant infestations in south Texas. Southwest Entomol 21(4):421–432 https://www.researchgate.net/publication/285540383_Using_spatial_information_technologies_for_detecting_and_mapping_whitefly_and_harvester_ant_infestations_in_South_Texas
Fayad YH, Badawy M, Wahby A (2003) Monitoring the biological control success of water hyacinth using multispectral SPOT images. Egypt J Biol Pest Co1 3(1):35–39
Fernando V, Dmitry B, Kevin P, John W, Felipe G (2018) A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 8(1):260–281. https://doi.org/10.3390/s18010260
Fitzgerald G (2000) Bug checking for mites – from the sky. Australian CottonGrower 21(1):29–31
Fletcher RS, Everitt JH (2007) A six-camera digital video imaging system sensitive to visible, red edge, near-infrared, and mid-infrared wavelengths. Geocarto Int 22(2):75–86. https://doi.org/10.1080/10106040701201608
Franke J, Menz G (2007) Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis Agric 8(3):161–172. https://doi.org/10.1007/s11119-007-9036-y
Hart SJ, Veblen TT (2015) Detection of spruce beetle-induced tree mortality using high-and medium-resolution remotely sensed imagery. Remote Sens Environ 168:134–145
Hart WG, Ingle SJ, Davis MR, Mangum C (1973) Aerial photography with infrared color film as a method of surveying for citrus blackfly. J Econ Entomol 66(1):190–194. https://doi.org/10.1093/jee/66.1.190
Hassan A, Kenawy MA, Kamal H, Abdel Sattar AA, Sowilem MM (2003) GIS-based prediction of malaria risk in Egypt. East Mediterr Health J (La Revue de Santé de la Méditerranée orientale) 9(4):548–558. https://pdfs.semanticscholar.org/f2b3/c080855306c33acdf817bdac73a8a1dd522d.pdf. Accessed 21 Feb 2005
Hayes RO, Maxwell EL, Mitchell CJ, Woodzik TL (1985) Detection, identification, and classification of mosquito larval habitats using remote sensing scanners in earth-orbiting satellites. Bull World Health Organ 63(2):361–374 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2536406/
Hobbs SE, Wolf WW (1989) An airborne radar technique for studying insect migration. Bull Entomol Res 79(4):693–704. https://doi.org/10.1017/S000748530001885X
Hu C, Deng YK, Wang R, Liu CJ, Long T (2016) High accuracy acquisition of 3-d flight trajectory of individual insect based on phase measurement. Sensors 16(12):2166. https://doi.org/10.3390/s16122166
Hu C, Kong S, Wang R, Zhang F (2019) Radar measurements of morphological parameters and species identification analysis of migratory insects. Remote Sens 11(17):1977. https://doi.org/10.3390/rs11171977
Huang Y, Lan YC, Hoffmann W (2008) Use of airborne multi-spectral imagery for area wide pest management. Agric Eng Int: CIGR Ejournal, Manuscript IT 07010:14 http://www.cigrjournal.org/index.php/Ejounral/article/view/1019/1012. Accessed Feb 2008
Hunt JER, Rondon SI (2017) Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. J Appl Remote Sens 11(2):026013. https://doi.org/10.1117/1.JRS.11.026013
Jackson HR, Wallen VR (1975) Microdensitometer measurements of sequential aerial photographs of field beans infected with bacterial blight. Phytopathol 65:961–968 https://www.apsnet.org/publications/phytopathology/backissues/Documents/1975Articles/Phyto65n09_961.PDF
Johansen K, Sallam N, Robson A, Samson P, Chandler K, Derby L, Eaton A, Jennings J (2018) Using GeoEye-1 imagery for multi-temporal object-based detection of canegrub damage in sugarcane fields in Queensland, Australia. GISci Remote Sen 55(2):285–305. https://doi.org/10.1080/15481603.2017.1417691
Johnson JB, Naiker M (2019) Seeing red: a review of the use of near-infrared spectroscopy (NIRS) in entomology. Appl Spectrosc Rev:1–30. https://doi.org/10.1080/05704928.2019.1685532
Kaur R, Jaidka M (2014) Spectral reflectance characteristics to distinguish Malva neglecta in wheat (Triticum aestivum). Indian J Agric Sci 84(10):1243–1249. http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/44208/19470. Accessed Oct 2014
Kuska MT, Mahlein A-K (2018) Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. Eur J Plant Pathol 152(4):987–992. https://doi.org/10.1007/s10658-018-1464-1
Lacaux JP, Tourre YM, Vignolle C, Ndione JA, Lafayet M (2007) Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley fever epidemic in Senegal. Remote Sens Environ 106(1):66–74. https://doi.org/10.1016/j.rse.2006.07.012
Maggiora R, Saccani M, Milanesio D, Porporato M (2019) An innovative harmonic radar to track flying insects: the case of Vespa velutina. Sci Rep 9:11964. https://doi.org/10.1038/s41598-019-48511-8
Malinowski R, Groom G, Schwanghart W, Heckrath G (2015) Detection and delineation of localized flooding from WorldView-2 multispectral data. Remote Sens 7(11):14853–14875. https://doi.org/10.3390/rs71114853
Meddens AJH, Hicke JA, Vierling LA, Hudak AT (2013) Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery. Remote Sens Environ 132:49–58. https://doi.org/10.1016/j.rse.2013.01.002
Meigs GW, Kennedy RE, Cohen WB (2011) A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens Environ 115:3707–3718. https://doi.org/10.1016/j.rse.2011.09.009
Mirik M, Ansley RJ, Price JA, Workneh F, Rush CM (2013) Remote monitoring of wheat streak mosaic progression using sub-pixel classification of Landsat 5 TM imagery for site specific disease management in winter wheat. Adv Remote Sens 2:16–28. https://doi.org/10.4236/ars.2013.21003
Morales FJ, Jones PG (2004) The ecology and epidemiology of whitefly-transmitted viruses in Latin America. Virus Res 100(1):57–65. https://doi.org/10.1016/j.virusres.2003.12.014
Nansen C, Elliott N (2016) Remote sensing and reflectance profiling in entomology. Annu Rev Entomol 61:139–158. https://doi.org/10.1146/annurev-ento-010715-023834
NASA’S Godard Space Flight Centre (2002) NASA researchers developing tools to help track and predict West Nile Virus. www.nasa.gov. 8 October2002.
Natural Resources Canada (2019) https://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9363
Nilsson HE (1995) Remote sensing and image analysis in plant pathology. Annu Rev Phytopathol 33:489–528. https://doi.org/10.1146/annurev.py.33.090195.002421
Odi-Lara M, Campos I, Neale CMU, Ortega-Farías S, Poblete-Echeverría C, Balbontín C, Calera A (2016) Estimating evapotranspiration of an apple orchard using a remote sensing-based soil water balance. Remote Sens 8(3):253. https://doi.org/10.3390/rs8030253
Oumar Z, Mutanga O (2011) The potential of remote sensing technology for the detection and mapping of Thaumastocoris peregrinus in plantation forests. South Forests: J Forest Sci 73(1):23–31. https://doi.org/10.2989/20702620.2011.574814
Peralta NR, Assefa Y, Du J, Barden CJ, Ciampitti IA (2016) Mid-season high-resolution satellite imagery for forecasting site-specific corn yield. Remote Sens 8:848–864. https://doi.org/10.3390/rs8100848
Piekarczyk J (2014) Application of remote sensing in agriculture. Geoinformatica Polonica 13:69–75. https://doi.org/10.2478/gein-2014-0007
Prabhakar M, Prasad YG, Thirupathi M, Sreedevi G, Dharajothi B, Venkateswarlu B (2011) Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Comput Electron Agric 7(9):189–198. https://doi.org/10.1016/j.compag.2011.09.012
Prasad YG, Prabhakar M (2012) Pest monitoring and forecasting. In: Abrol DB, Shankar U (eds) Integrated Pest Management. CAB International, Wallingford, pp 41–57
Pretorius ZA, Lan CX, Prins R, Knight V, McLaren NW, Singh RP, Bender CM, Kloppers FJ (2017) Application of remote sensing to identify adult plant resistance loci to stripe rust in two bread wheat mapping populations. Precis Agric 8(4):411–428. https://doi.org/10.1007/s11119-016-9461-x
Qin Z, Zhang M (2005) Detection of rice sheath blight for in-season disease management using multispectral remote sensing. Int J Appl Earth Obs 7(2):115–128. https://doi.org/10.1016/j.jag.2005.03.004
Qin Z, Zhang M, Christensen T, Li W, Tang H (2003) Remote sensing analysis of rice disease stresses for farm pest management using wide-band airborne data. In Proc IEEE International Geoscience and Remote Sensing Symposium. pp: 2215-2217 https://doi.org/10.1109/IGARSS.2003.1294393
Rani AS, Jyothi S (2017) A study on hyper spectral remote sensing pest management. Int J Recent Innov Trends Comput Commun 5(6):497–503. http://www.ijritcc.org/index.php/ijritcc/issue/view/8. Accessed June 2017
Rani DS, Venkatesh MN, SriCh NS, Kumar KA (2018) Remote sensing as pest forecasting model in agriculture. Int J Curr Microbiol Appl Sci 7(3):280–2689. https://doi.org/10.20546/ijcmas.2018.703.310
Reynolds DR, Riley JR (1997) The flight behaviour and migration of insect pests: radar studies in developing countries. Chatham: Natural Resources Institute. NRI Bulletin no. 71.
Riley JR, Reynolds DR, Smith AD, Edwards AS, Zhai BP (1995) Observations of the autumn migration of the rice leaf roller cnaphalocrocis medinalis (Lepidoptera: Pyralidae) and other moths in eastern china. Bull Entomol Res 85(3):397–414. https://doi.org/10.1017/S0007485300036130
Robertson C, Wulder M, Nelson T, White J (2008) Risk rating for mountain pine beetle infestation of lodgepole pine forests over large areas with ordinal regression modelling. Forest Ecol Manag 256:900–912. https://doi.org/10.1016/j.foreco.2008.05.054
Roger D, Randolph SE (1991) Mortality rates and population density of Tsetse flies correlated with satellite imagery. Nature 351(6329):734–741. https://doi.org/10.1038/351739a0
Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72(1):1–13. http://128.227.177.113/pa/Publications/Sankaran_Mishra_Ehsani_Davis_2010.pdf. Accessed June 2010
Senf C, Seidl R, Hostert P (2017) Remote sensing of forest insect disturbances: Current state and future directions. Int J Appl Earth Obs 60:49–60. https://doi.org/10.1016/j.jag.2017.04.004
Sivanpillai R, Latchininsky AV (2013) Publisher’s Note: Special section on advances in remote sensing applications for locust habitat monitoring and management. J Appl Remote Sens 7(1):075001.33. https://doi.org/10.1117/1.JRS.7.075001
Smith AD, Riley JR, Gregory RD (1993) A method for routine monitoring of the aerial migration of insects by using a vertical looking redar. Philos Trans R Soc Lond B 340:393–404. https://doi.org/10.1098/rstb.1993.0081
Toler RW, Smith BD, Harlan JC (1981) Use of aerial color infrared to evaluate crop disease. Plant Dis 65(1):24–31. https://doi.org/10.1094/pd-65-24
Wang Y, Nansen C, Zhang Y (2016) Integrative insect taxonomy based on morphology, mitochondrial DNA and hyperspectral reflectance profiling. Zool J Linnean Soc 177:378–394. https://doi.org/10.1111/zoj.12367
Westbrook JK, Eystera RS, Wolf WW, Lingrena PD, Raulstonb JR (1995) Migration pathways of corn earworm (Lepidoptera: Noctuidae) indicated by tetroon trajectories. Agric For Meteorol 73(1):67–87. https://doi.org/10.1016/0168-1923(94)02171-F
Yan Y, Feng C-C, Chang KTT (2017) Towards enhancing integrated pest management based on volunteered geographic information. Int J Geo Inform 6(7):224. https://doi.org/10.3390/ijgi6070224
Yang C (2012) A high-resolution airborne four-camera imaging system for agricultural remote sensing. Comput Electron Agric 88:13–24. https://doi.org/10.1016/j.compag.2012.07.003
Yang C, Everitt JH (2011) Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations, In: Thompson, Winston, M.O. (Eds.), The Whitefly, Bemisia tabaci (Homoptera: Aleyrodidae) Interaction with Geminivirus-Infected Host Plants: Bemisia tabaci, Host Plants and Geminiviruses. Springer, Netherlands. pp: 357-381. https://doi.org/10.1007/978-94-007-1524-0_13
Yang C, Everitt JH, Davis MR, Mao C (2003) A CCD camera-based hyperspectral imaging system for stationary and airborne applications. Geocarto Int 18(2):71–80. https://doi.org/10.1080/10106040308542274
Yang C, Everitt JH, Bradford JM, Murden D (2009a) Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield. Trans ASABE 52(2):641–649. https://doi.org/10.13031/2013.26816)@2009
Yang Z, Rao MN, Elliott NC, Kindler SD, Popham TW (2009b) Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Comput Electron Agric 67(1):64–70. https://doi.org/10.1016/j.compag.2009.03.003
Yoshihisa F, Yoko F, Yoshiyuk Y, Shogo O, Koichi N, Tadao N, Tsyoshi Y, Yuji I (2007) Nondestructive detection of termites using a millimeter-wave imaging technique. Forest Prod J 57(10):75–79 https://www.engineeringvillage.com/search/quick.url?SEARCHID=a1a4044128cd4ee3bbcab7962df9da70&COUNT=1&usageOrigin=&usageZone=#foo
Zhang J, Yang C, Song H, Hoffmann WC, Zhang D, Zhang G (2016) Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sens 8(3):257. https://doi.org/10.3390/rs8030257
Zhang J, Wang X, Yang C, Zhang J, He D, Song H (2018) Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras. Comput Electron Agric 151:196–206. https://doi.org/10.1016/j.compag.2018.06.010
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(3):868–887. https://doi.org/10.3390/s18030868
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Abd El-Ghany NM had the idea for the review article. All authors performed the literature search. Abd El-Ghany NM wrote and drafted the paper, and Abd El-Ghany NM, Abd El- Aziz SE, and Marei SS critically revised the work.
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Abd El-Ghany, N.M., Abd El-Aziz, S.E. & Marei, S.S. A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environ Sci Pollut Res 27, 33503–33515 (2020). https://doi.org/10.1007/s11356-020-09517-2
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DOI: https://doi.org/10.1007/s11356-020-09517-2