Advertisement

Applications of Remote Sensing in Pest Monitoring and Crop Management

  • Karim Ennouri
  • Mohamed Ali Triki
  • Abdelaziz Kallel
Chapter

Abstract

Precision agricultural skill has constructed and will still construct the road we are moving into this novel theory of precision agriculture. By increasing the inspection and appliance of inputs on the land, farmers are changing from a usual, standardized treatment of every agricultural land to a perfect treatment for as little as possible districts. Remote sensing processes offer a basis for which vegetal stress and growth reaction can be estimated. Remote sensing research based on terrestrial and spatial domains has demonstrated that numerous kinds of plant illness, through pre-visual infection signs for pathogens, hostile species and also plant health indicators, can be identified through aerial hyperspectral imaging. Inspecting foliage using remote sensing data necessitates understanding of the organization and role of foliage and its reflectance characteristics. Sensors have been ameliorated to calculate the reflectance of incident bright at numerous wavebands and have been associated to plant evolution and plant cover. Remote sensing technology has the major advantage to obtaining data about a given entity or region without having physical exchange and frequently employs surface-based instruments or spatial pictures. Remote sensing would be considered as an economic and relevant instrument for land-scale pest controlling and study.

Keywords

Remote sensing Plant Illness Reflectance Precision agriculture 

References

  1. Adam E, Mutanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl Ecol Manag 18(3):281–296CrossRefGoogle Scholar
  2. Agam N, Kustas WP, Anderson MC, Li F, Neale CM (2007) A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens Environ 107(4):545–558CrossRefGoogle Scholar
  3. Aiazzi B, Alparone L, Baronti S, Lastri C, Selva M (2012) Spectral distortion in lossy compression of hyperspectral data. J Electrical Comput Eng 2012:3CrossRefGoogle Scholar
  4. Anderson MC, Norman JM, Mecikalski JR, Otkin JA, Kustas WP (2007) A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J Geophys Res Atmos 112(D11)Google Scholar
  5. Barton CV (2012) Advances in remote sensing of plant stress. Plant Soil 354(1–2):41–44CrossRefGoogle Scholar
  6. Bioucas-Dias JM, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N, Chanussot J (2013) Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci Remote Sens Mag 1(2):6–36CrossRefGoogle Scholar
  7. Blake W, Hongjie X, Paul J (2005) Early detection of oak wilt disease in quercus ssp.: a hyperspectral approach. Pecora 16 “Global Priorities in Land Remote Sensing” October 23–27, 2005 ∗ Sioux Falls, South Dakota, USAGoogle Scholar
  8. Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press, New YorkGoogle Scholar
  9. Chen N, Zhang X, Wang C (2015) Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring. Comput Electron Agric 111:78–91CrossRefGoogle Scholar
  10. Dandois JP, Ellis EC (2010) Remote sensing of vegetation structure using computer vision. Remote Sens 2(4):1157–1176CrossRefGoogle Scholar
  11. Erener A (2011) Remote sensing of vegetation health for reclaimed areas of Seyitömer open cast coal mine. Int J Coal Geol 86(1):20–26CrossRefGoogle Scholar
  12. Fenghua W, Shujuan Z (2008) Research progress of the farming information collections key technologies on precision agriculture. Trans Chin Soc Agric Mach 39(5):112–121Google Scholar
  13. Frohn RC, Lopez RD (2017) Remote sensing for landscape ecology: new metric indicators: monitoring, modeling, and assessment of ecosystems. CRC PressGoogle Scholar
  14. Gebbers R, De Bruin S (2010) Application of geostatistical simulation in precision agriculture. In: Geostatistical applications for precision agriculture. Springer, Dordrecht, pp 269–303CrossRefGoogle Scholar
  15. Gupta RP (2017) Remote sensing geology. SpringerGoogle Scholar
  16. Khosla R (2010) Precision agriculture: challenges and opportunities in a flat world. In: 19th World Congress of Soil Science, soil solutions for a changing world. Brisbane, AustraliaGoogle Scholar
  17. Koleshko VM, Gulay AV, Polynkova EV, Gulay VA, Varabei YA (2012) Intelligent systems in technology of precision agriculture and biosafety. In: Intelligent systems. InTechGoogle Scholar
  18. Kuenzer C, Bluemel A, Gebhardt S, Quoc TV, Dech S (2011) Remote sensing of mangrove ecosystems: a review. Remote Sens 3(5):878–928CrossRefGoogle Scholar
  19. Lake JV, Bock GR, Goode JA (2008) Precision agriculture: spatial and temporal variability of environmental quality (vol. 210). WileyGoogle Scholar
  20. Landgrebe DA (2005) Signal theory methods in multispectral remote sensing (vol. 29). WileyGoogle Scholar
  21. Lausch A, Erasmi S, King DJ, Magdon P, Heurich M (2016) Understanding forest health with remote sensing-part I—a review of spectral traits, processes and remote-sensing characteristics. Remote Sens 8(12):1029CrossRefGoogle Scholar
  22. Lausch A, Borg E, Bumberger J, Dietrich P, Heurich M, Huth A et al (2018) Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sens 10(7):1120CrossRefGoogle Scholar
  23. Lawley V, Lewis M, Clarke K, Ostendorf B (2016) Site-based and remote sensing methods for monitoring indicators of vegetation condition: an Australian review. Ecol Indic 60:1273–1283CrossRefGoogle Scholar
  24. Lillesand T et al (2014) Remote sensing and image interpretation. John Wiley & Sons, HobokenGoogle Scholar
  25. Nutter FW, Tylka GL Jr, Guan J, Moreira AJD, Marett CC, Rosburg TR, Basart JP, Chong CS (2002) Use of remote sensing to detect soybean cyst nematode-induced plant stress. J Nematol 34(3):222–231Google Scholar
  26. Ozdogan M, Yang Y, Allez G, Cervantes C (2010) Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sens 2(9):2274–2304CrossRefGoogle Scholar
  27. Peijun DU, Xingli LI, Wen CAO, Yan LUO, Zhang H (2010) Monitoring urban land cover and vegetation change by multi-temporal remote sensing information. Min Sci Technol (China) 20(6):922–932CrossRefGoogle Scholar
  28. Rees WG, Pellika P (2010) Principles of remote sensing. Remote Sensing of Glaciers. LondonGoogle Scholar
  29. Sabins FF (2007) Remote sensing: principles and applications. Waveland PressGoogle Scholar
  30. Schellberg J, Hill MJ, Gerhards R, Rothmund M, Braun M (2008) Precision agriculture on grassland: applications, perspectives and constraints. Eur J Agron 29(2–3):59–71CrossRefGoogle Scholar
  31. Schowengerdt RA (2006) Remote sensing: models and methods for image processing. Academic Press, OrlandoGoogle Scholar
  32. Schowengerdt RA (2012) Techniques for image processing and classification in remote sensing. Academic Press, San DiegoGoogle Scholar
  33. Singh HB, Jha A, Keswani C (eds) (2016a) Intellectual property issues in biotechnology. CABI, Wallingford. 304 pages, ISBN-13:9781780646534Google Scholar
  34. Singh HB, Jha A, Keswani C (2016b) Biotechnology in agriculture, medicine and industry: an overview. In: Singh HB, Jha A, Keswani C (eds) Intellectual property issues in biotechnology. CABI, Wallingford, pp 1–4CrossRefGoogle Scholar
  35. Singh HB, Sarma BK, Keswani C (eds) (2017) Advances in PGPR research. CABI, Wallingford. 408 pages, ISBN-9781786390325Google Scholar
  36. Thenkabail PS, Lyon JG (2016) Hyperspectral remote sensing of vegetation. CRC PressGoogle Scholar
  37. Thilakarathna M, Raizada M (2018) Challenges in using precision agriculture to optimize symbiotic nitrogen fixation in legumes: progress, limitations, and future improvements needed in diagnostic testing. Agronomy 8(5):78CrossRefGoogle Scholar
  38. Twomey S (2013) Introduction to the mathematics of inversion in remote sensing and indirect measurements (vol. 3). ElsevierGoogle Scholar
  39. Ulaby, F. T., Long, D. G., Blackwell, W. J., Elachi, C., Fung, A. K., Ruf, C., et al. (2014). Microwave radar and radiometric remote sensing (4, 5, 6). Ann Arbor: University of Michigan PressGoogle Scholar
  40. Wang J, Sammis TW, Gutschick VP, Gebremichael M, Dennis SO, Harrison RE (2010) Review of satellite remote sensing use in forest health studies. Open Geogr J 3(1):28–42CrossRefGoogle Scholar
  41. Winstead AT, Norwood SH, Griffin TW, Runge M, Adrian AM, Fulton J, Kelton J (2010) Adoption and use of precision agriculture technologies by practitioners. In: Proc. the 10th International Conference on Precision Agriculture. pp 18–21Google Scholar
  42. Zarco-Tejada PJ, Camino C, Beck PSA, Calderon R, Hornero A, Hernández-Clemente R, Gonzalez-Dugo V (2018) Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat Plants 4(7):432CrossRefGoogle Scholar
  43. Zargar A, Sadiq R, Naser B, Khan FI (2011) A review of drought indices. Environ Rev 19.(NA:333–349CrossRefGoogle Scholar
  44. Zhang C, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13(6):693–712CrossRefGoogle Scholar
  45. Zhang YP, Guo JB, Wang S, Wang HG, Ma ZH (2009) Relativity research on near ground and satellite remote sensing reflectance of wheat stripe rust (in Chinese). ActaPhytophylacica Sin 36:119–122Google Scholar
  46. Zhihao Q, Minghua Z, Thomas C, Wenjuan L, Huajun T (2003) Remote sensing analysis of rice disease stresses for farm pest management using wide-band airborne data. International Geosciences and Remote Sensing Symposium, IV: 2215–2217, July 21-25, 2003, Toulouse, FranceGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Karim Ennouri
    • 1
    • 2
  • Mohamed Ali Triki
    • 2
  • Abdelaziz Kallel
    • 1
  1. 1.Digital Research Centre of SfaxTechnopark of SfaxSfaxTunisia
  2. 2.Laboratory of Amelioration and Protection of Olive Genetic Resources, Olive Tree InstituteUniversity of SfaxSfaxTunisia

Personalised recommendations