Skip to main content
SpringerLink
Log in

Applications of Geospatial Technologies in Plant Health Management

  • Chapter
  • First Online:
Innovative Pest Management Approaches for the 21st Century

  • 782 Accesses

Abstract

In this chapter, an overview of remote sensing applications for pest management and plant protection is presented. The flow and gaps in the existing organization of plant protection information are highlighted. Methods of integration of remotely sensed data into the conventional plant protection and crop assessment system are addressed. Crop pests and diseases commonly occurring in continuous cropping pattern zones, whose symptoms are amenable to remote sensing, are dealt with. Numerous economically important crop pests/diseases are sporadic in time and space, but they are not included in this chapter. The objective of this chapter is to create basic awareness for the possibility of using remotely sensed data for pest detection and plant protection. This will also enthuse further thinking to make this emerging area of application operational in the years to come.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Ashal C (1987) Needless disaster. Disasters:11–17

    Google Scholar 

  • Bald BL, Steadman JR, Weiss A (1978) Canopy structure irrigation influence on white mould disease and microclimate. Phytopathology 68:14311437

    Google Scholar 

  • Bansil PC (1984) Agricultural statistics in India – a guide. Oxford and IBH Publishing, New Delhi

    Google Scholar 

  • Barrett EC (1980) Satellite monitoring of conditions conducive to the upsurge of insect pests, Satellite Remote Sensing Applications to rural disasters. In: Proceedings of joint ESA FAO/WMO international training course, Rome, Italy, 27 Oct–7 Nov 1980 ESA SP 1035, pp 105–111

    Google Scholar 

  • Boochs F, Kupfer G, Dockter K, Kuhbauch W (1990) Shape of the red edge as vitality indicator for plants. Int J Remote Sens 11(10):1741–1753

    Article  Google Scholar 

  • Buschmann C, Rinderle U, Lichtenthaler HK (1991) Detection of stress in coniferous forest trees with the VIRAF spectrometer. IEEE Trans Geosci Remote Sens 29(1):96–100

    Article  Google Scholar 

  • Cardenas R, Gausman HE, Allen WA, Schupp M (1969) The influence of ammonia induced cellular discoloration within cotton leaves on light reflectance, transmittance and absorptance. Remote Sens Environ 1:199–202

    Article  Google Scholar 

  • Collins W (1978) Remote sensing of crop type and maturity. Photogramm Eng Remote Sens 44:737–749

    Google Scholar 

  • Colwell RN (1964) Aerial photography - a valuable sensor for the scientist. Am Sci 52:16–49

    Google Scholar 

  • Colwell JE (1974) Vegetation canopy reflectance. Remote Sens Environ 3:175–183

    Article  Google Scholar 

  • Cramer HH (1967) Plant protection and world crop production. Pflanzenschutz - Nachrichten, Bayer 20:1–524

    Google Scholar 

  • Dakshinamurthi C, Krishnamurthy B, Summanwar AS, Shanta P, Pisharoty PR (1971) Preliminary investigations on the root wilt disease in coconut plants. In Kerala State, India by remote sensing techniques. In: Proceedings of International Astronautical Federation Congress, Konstanz, West Germany

    Google Scholar 

  • Elvidge CD (1990) Visible and near infrared reflectance characteristics of dry plant materials. Int J Remote Sens 11(10):1775–1795

    Article  Google Scholar 

  • Gausman HW (1974) Leaf reflectance of near infrared radiation. Photogramm Eng 40:183–191

    Google Scholar 

  • Gausman HW, Escobar DE, Rodriguez RR, Thomas CW, Bowen RL (1978) Ozone damage detection in cantaloupe plants. Photogramm Eng Remote Sens 44:481–485

    Google Scholar 

  • Goel NS (1982) A review of crop canopy reflectance models, final report, contract NA 59-16505, August 1982. NASA, Houston

    Google Scholar 

  • Grant L (1987) Diffuse and specular characteristics of leaf reflectance. Remote Sens Environ 22:309322

    Article  Google Scholar 

  • Hielkema JU, Roffey J, Tucker CJ (1986) Assessment of ecological conditions associated with the 1980-81 desert locust plague upsurge in West Africa using environmental satellite data. Int J Remote Sens 7(11):1609–1622

    Article  Google Scholar 

  • Horler DNH, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288

    Article  Google Scholar 

  • Idso SB, Jackson RD, Reginato RJ (1977) Remote sensing of crop yields. Science 196:1925

    Article  Google Scholar 

  • IRRI (International Rice Research Institute) (1977) Brown plant-hopper biotypes complicate resistance breeding. The IRRI Reporter, 2/77 (October, 1977)

    Google Scholar 

  • ISRO (Indian Space Research Organisation) (1978) Identification and classification of paddy and sugarcane crops around Mandya, ISRO-TN-07-78

    Google Scholar 

  • Jackson RD (1982) Canopy temperatures and crop water stress. In: Hillel D (ed) Advances in irrigation, vol 1. Academic, New York

    Google Scholar 

  • Kalubarme MH, Vyas SP (1988) Rice acreage estimation in Midnapore district using Landsat MSS digital data. In: Proceedings of national symposium on remote sensing in rural development, 17 Nov 1988. Indian Society of Remote Sensing, pp 229–234

    Google Scholar 

  • Kamat DS, Sinha SK (1984) Proceedings of the seminar on crop growth conditions and remote sensing, 22–23 June 1984. IARI, New Delhi

    Google Scholar 

  • Kamath DS, Gopalan AKS, Ajai Shashikumar MN, Sinha SK, Chaturvedi GS, Singh AK (1985) Assessment of water-stress effects on crops. Int J Remote Sens 6(3):577–589

    Article  Google Scholar 

  • Kauth RJ, Thomas GS (1976) Tasseled cap - a graphic description of the spectral temporal development of agricultural crops as seen by Landsat. In: Proceedings of the symposium machine processing of remotely sensed data. LARS, Purdue

    Google Scholar 

  • Knipling EB (1970) Physical and physiological basis for the reflectance in the visible and near infrared radiation from vegetation. Remote Sens Environ 1:155–159

    Article  Google Scholar 

  • Kumar M (1988) Crop canopy spectral reflectance. Int J Remote Sens 9(2):285–290

    Article  Google Scholar 

  • Lan Y, Thomson SJ, Huang Y, Hoffmann WC, Zhang H (2010) Current status and future directions of precision aerial application for site-specific crop management in the USA. Comput Electron Agric 74(1):34–38

    Article  Google Scholar 

  • Lichtenthaler HK (1988) Applications of chlorophyll fluorescence in photosynthesis research, stress physiology, hydrobiology and remote sensing. Kluwer Academic, Dordrect

    Book  Google Scholar 

  • Lillesand TM, Kiefer RW (1979) Remote sensing and image interpretation. Wiley, New York

    Google Scholar 

  • Lorenzen B, Jensen A (1989) Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing Environ 27:201–209

    Article  Google Scholar 

  • Miller JR, Hare EW, Wu J (1990) Quantitative characterization of the vegetation red edge reflectance, 1. An inverted – Gaussian reflectance model. Int J Remote Sens 11(10):1755–1773

    Article  Google Scholar 

  • Monteith JL, Szeicz G (1962) Radiative temperature in the heat balance of natural surfaces. Q J Roy Meteorol Soc 88:496–507

    Article  Google Scholar 

  • Nagarajan S (1983) Plant disease epidemiology. Oxford and IBH Publishing, New Delhi

    Google Scholar 

  • Nagarajan S, Singh H (1976) Preliminary studies on forecasting wheat stem rust appearance. Agric Meteorol 17:281–289

    Article  Google Scholar 

  • Nagarajan S, Sieboldt G, Krauz J, Sarri EE, Joshi LM (1982) Utility of weather satellites in monitoring cereal rust epidemics. SPfl SchutzPfl Krankh 89:276–281

    Google Scholar 

  • Nagarajan S, Shashikumar MN, Ajai, Kamat DS (1984) Detection of wheat rust disease from Landsat multispectral data. In: Proceedings of the seminar on crop growth conditions and remote sensing, 22–23 June. IARI, New Delhi

    Google Scholar 

  • Nageswara Rao PP, Rao VR (1987) Rice crop identification and area estimation using remotely sensed data from Indian cropping patterns. Int J Remote Sens 8:639–650

    Article  Google Scholar 

  • Näsi R, Honkavaara E, Lyytikäinen-Saarenmaa P, Blomqvist M, Litkey P, Hakala T, Holopainen M (2015) Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens (Basel) 7(11):15467–15493

    Article  Google Scholar 

  • Nisarga I, Srikanth P, Nageswara Rao PP (2019) Cotton crop production estimationusing Sentinel-2A Multi Spectral Instrument in Raichur district, Karnataka, India. In: Proceedings of the national symposium on innovations in geospatial technology for sustainable development with special emphasis on NER, pp 133–134

    Google Scholar 

  • Pennsylvania State University (2019) Using geospatial technology for pest monitoring and detection. Pennsylvania State University, University Park

    Google Scholar 

  • Pinter PJ Jr, Hatfield JL, Schepers JS, Barnes EM, Moran MS, D, C. S. T., Upchurch DR (2003) Remote sensing for crop management. Publications from USDA-ARS/UNL Faculty. 1372. http://digitalcommons.unl.edu/usdaarsfacpub/1372

  • Purdom JFW (1973) Meso-heights and satellite imagery. Money Wealth Rev 101:180–181

    Article  Google Scholar 

  • Rani D, Sudha MN, Venkatesh, NagaSatya S, AnandKumar K (2018) Remote sensing as pest forecasting model in agriculture. Int J Curr Microbiol Appl Sci 7(3):2680–2689

    Article  Google Scholar 

  • RaoKrishna MV, Ayyangar RS, Nageswara Rao PP (1982) Role of multispectral data in assessing crop management and crop yield. In: Proceedings of 8th machine processing of remote sensing data, West Lafayette, 7–9 July 1982, pp 226–233

    Google Scholar 

  • Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43(12):1541–1552

    Google Scholar 

  • Riley JR (1989) Remote sensing in entomology. Annu Rev Entomol 34:247–271

    Article  Google Scholar 

  • Rock BN, Vogelmann JE, Williams DL, Vogelmann AF, Hoshizaki T (1986) Remote detection of forest damage. Bioscience 36:439–445

    Article  Google Scholar 

  • Ross JK, Marshak AL (1988) Calculation of canopy bidirectional reflectance using the Monte Carlo method. Remote Sens Environ 24:213–225

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Vegetation systems in the great plains with ERTS. In: 3rd ERTS symposium. NASA SP-351, vol 1, pp 309–317

    Google Scholar 

  • Sabtu NM, Idris NH, Ishak MHI (2018) The role of geospatial in plant pests and diseases; an overview. I O P conference series. Earth Env Sci 169:012–013. https://doi.org/10.1088/1755-1315/169/1/012013

    Article  Google Scholar 

  • Sahai B, Ajai (1988) Application of remote sensing techniques in agriculture. Fertilizer News 33(4):59–65

    Google Scholar 

  • Sellers PJ (1985) Canopy photosynthesis and transpiration. Sensors 6:1335–1372

    Google Scholar 

  • Shenk JS, Landa I, Hoover MR, Westerhaus MA (1981) Description and evaluation of a near infrared reflectance spectra-computer for forage and grain analysis. Crop Sci 21:355–358

    Article  CAS  Google Scholar 

  • Suits GH (1972) The calculation of the directional reflectance of a vegetation canopy. Remote Sens Environ 2:117–125

    Article  Google Scholar 

  • Tucker CJ (1978) Are two photographic infrared sensors required. Photogramm Eng Remote Sens 44(3):289–295

    Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • Tucker CJ, Garratt MW (1977) Leaf optical system modeled as a stochastic process. Appl Optics 16(3):635–642

    Article  CAS  Google Scholar 

  • Tucker CJ, Hielkema JU, Roffey J (1985) The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity. Int J Remote Sens 6(1):127–138

    Article  Google Scholar 

  • UNO Newsletter (2019) Satellite data key part of early warning system for plant pest infestations. United Nations Office for Outer Space Affairs, pp 1–2

    Google Scholar 

  • Vanegas F, Bratanov D, Powell K, Weiss J, Gonzalez F (2018) A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 18(1):260

    Article  Google Scholar 

Download references

Acknowledgement

The authors are thankful to Director, KSRSAC for his interest and encouragement in pursuing this new field of space application. The authors gratefully acknowledge the work done by several authors whose references are mentioned in the literature cited here. Thanks are due to the support staff at KSRSAC for their secretarial support.

Author information

Authors and Affiliations

  1. Karnataka State Remote Sensing Application Centre (KSRSAC), Bengaluru, Karnataka, India

    P. P. Nageswara Rao & B. P. Lakshmikantha

Authors
  1. P. P. Nageswara Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. P. Lakshmikantha
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Society for Science and Technology Applications (SSTA), Bangalore, Karnataka, India

    Akshay Kumar Chakravarthy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nageswara Rao, P.P., Lakshmikantha, B.P. (2020). Applications of Geospatial Technologies in Plant Health Management. In: Chakravarthy, A. (eds) Innovative Pest Management Approaches for the 21st Century. Springer, Singapore. https://doi.org/10.1007/978-981-15-0794-6_1

Download citation

Publish with us

Policies and ethics

Search

Navigation