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Remote Sensing from RPAS in Agriculture: An Overview of Expectations and Unanswered Questions

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Advances in Service and Industrial Robotics (RAAD 2017)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 49))

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Abstract

Agriculture and Remote Sensing (RS) have shared a long common story. Spectral properties of vegetation can be related to many phenol-/physiological parameters of crop. The recent technology advance has made available for users both low cost multispectral sensors and platforms (Remotely Piloted Aerial Systems, RPAS). In Precision Farming the current moment is crucial, since scientists have still not answered all the questions concerning performances of RS+RPAS systems, nor consistency of costs with those required by the low profit agricultural sector. We, firstly, try to lists the main tasks that are expected from RS+RPAS in agriculture (energy balance and thermal remote sensing excluded). Finally a discussion is opened about those critical aspects that, in our opinion, make the current adoption of RS+RPAS still unreliable, or not still proper, in agriculture.

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Notes

  1. 1.

    A prescription map (PM) is a georeferenced representation of a crop field showing a zonation defining the amount of a certain soil amendment, or the intensity of a management practice, that has to be locally released, or performed.

  2. 2.

    Vigor Maps are maps where the local vegetative strength of crop is mapped according to some spectral properties imaged by multispectral sensors.

References

  1. Cook SE, Bramley RGV (1998) Precision agriculture – opportunities, benefits and pitfalls on site-specific crop management in Australia. Aust J Exp Agric 38:753–763

    Article  Google Scholar 

  2. Borgogno-Mondino E, Lessio A, Tarricone L, Novello V, de Palma L (in press) A comparison between multispectral aerial and satellite imagery in precision viticulture. Precis Agric

    Google Scholar 

  3. Grenzdörffer GJ, Engel A, Teichert B (2008) The photogrammetric potential of low-cost UAVs in forestry and agriculture. Int Arch Photogrammetry Remote Sens Spat Inf Sci 31(B3):1207–1214

    Google Scholar 

  4. Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 38(1–2):95–120

    Article  Google Scholar 

  5. Hall A, Lamb DW, Holzapfel B, Louis J (2002) Optical remote sensing applications in viticulture – a review. Aust J Grape Wine Res 8:36–47

    Article  Google Scholar 

  6. Testa S, Borgogno Mondino E, Pedroli C (2014) Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates. Eur J Remote Sens 47:285–305

    Article  Google Scholar 

  7. Sauerbier M, Siegrist E, Eisenbeiss, H, Demir N (2011) The practical application of RPAS-based photogrammetry under economic aspects. Int Arch Photogrammetry Remote Sens Spat Inf Sci 38(1)

    Google Scholar 

  8. Lee IS, Lee JO, Kim SJ, Hong SH (2013) Orhtophoto accuracy assessment of ultra-light fixed wing RPAS photogrammetry techniques. J Korean Soc Civil Eng 33(6):2593–2600

    Article  Google Scholar 

  9. Boccardo P, Chiabrando F, Dutto F, Tonolo FG, Lingua A (2015) UAV deployment exercise for mapping purposes: evaluation of emergency response applications. Sensors 15(7):15717–15737

    Article  Google Scholar 

  10. Rey C, Martin MP, Lobo A, Luna I, Diago MP, Millan B, Tardaguila J (2013). Multispectral imagery acquired from a RPAS to assess the spatial variability of Tempranillo vineyard. In: Proceedings of precision agriculture 2013 - 9th European conference on precision agriculture, ECPA 2013, pp 617–624

    Google Scholar 

  11. Smith GM, Milton EJ (2010) The use of the empirical line method to calibrate remotely sensed data to reflectance. Int J Remote Sens 20(13):2653–2662. doi:10.1080/014311699211994

    Article  Google Scholar 

  12. Matese A, Toscano P, Di Gennaro SF, Genesio L, Vaccari FP, Primicerio J, Gioli B (2015) Intercomparison of RPAS, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens 7(3):2971–2990

    Article  Google Scholar 

  13. Erena M, Montesinos S, Portillo D, Alvarez J, Marin C, Henarejos JM, Fernandez L, Ruiz LA (2016) Configuration and specifications of an unmanned aerial vehicle for precision agriculture. ISPRS Int Arch Photogrammetry Remote Sens Spat Inf Sci 809–816

    Google Scholar 

  14. Ristorto G, Mazzetto F, Guglieri, G, Quagliotti F (2015) Monitoring performances and cost estimation of multirotor unmanned aerial systems in precision farming. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE Press, pp 502–509

    Google Scholar 

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Correspondence to Enrico Borgogno Mondino .

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Borgogno Mondino, E. (2018). Remote Sensing from RPAS in Agriculture: An Overview of Expectations and Unanswered Questions. In: Ferraresi, C., Quaglia, G. (eds) Advances in Service and Industrial Robotics. RAAD 2017. Mechanisms and Machine Science, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-61276-8_51

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  • DOI: https://doi.org/10.1007/978-3-319-61276-8_51

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-61276-8

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