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A flexible unmanned aerial vehicle for precision agriculture

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Abstract

An unmanned aerial vehicle (“VIPtero”) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, “VIPtero” performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10 min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops.

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References

  • Berni, J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47, 722–738.

    Article  Google Scholar 

  • Bramley, R. G. V., Ouzman, J., & Boss, P. K. (2011). Variation in vine vigour, grape yield and vineyard soils and topography as indicators of variation in the chemical composition of grapes, wine and wine sensory attributes. Australian Journal of Grape and Wine Research, 17, 217–229.

    Article  CAS  Google Scholar 

  • Costa-Ferreira, A. M., Germain, C., Homayouni, S., Da Costa, J. P., Grenier, G., Marguerit, E., et al. (2007). Transformation of high resolution aerial images in vine vigour maps at intra-block scale by semi-automatic image processing (pp. 1372–1381). Poreč: Proceedings of XV International Symposium GESCO.

    Google Scholar 

  • Delenne, C., Durrieu, S., Rabatel, G., & Deshayes, M. (2010). From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data. Computers and Electronics in Agriculture, 70, 78–83.

    Article  Google Scholar 

  • Fiorillo, E., Genesio, L., Maselli, F., De Filippis, T., Gioli, B., & Toscano, P. (2009). Mapping the spatial variability of vineyard canopy using high-resolution airborne multispectral images. Proceedings of 16th International Symposium GiESCO. Group of International Experts of Vitivinicultural Systems for Cooperation (pp. 19–24). Davis, CA: UC Davis.

  • Gay, A. P., Stewart, T. P., Angel, R., Easey, M., Eves, A. J., Thomas, N. J., Pearce, D. A., & Kemp, A. I. 2009. Developing unmanned aerial vehicles for local and a flexible environmental and agricultural monitoring. Proceedings of RSPSoc 2009 Annual Conference. RSPSoc (pp. 471–476) Curran Associates, Inc., Leicester.

  • Goulet, E., & Morlat, R. (2011). The use of surveys among wine growers in vineyards of the middle-Loire Valley (France), in relation to terroir studies. Land Use Policy, 28, 770–782.

    Article  Google Scholar 

  • Goward, S. N., Markham, B., Dye, D. G., Dulaney, W., & Yang, J. L. (1991). Normalized difference vegetation index measurements from the advanced very high-resolution radiometer. Remote Sensing of Environment, 35, 257–277.

    Article  Google Scholar 

  • Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., et al. (2004). Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44, 49–61.

    Article  Google Scholar 

  • Huglin, P., & Schneider, C. (1998). Biologie et écologie de la vigne. Paris: Tec & Doc Lavoisier.

    Google Scholar 

  • Johnson, L. F., Bosch, D. F., Williams, D. C., & Lobitz, B. M. (2001). Remote sensing of vineyard management zones: Implications for wine quality. Applied Engineering in Agriculture, 17, 557–560.

    Google Scholar 

  • Lamb, D. W., Weedon, M. M., & Bramley, R. G. V. (2004). Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution. Australian Journal of Grape and Wine Research, 10, 46–54.

    Article  CAS  Google Scholar 

  • Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbe, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8, 3557–3585.

    Article  Google Scholar 

  • Liang, C. K., Chang, L. W., & Chen, H. H. (2008). Analysis and compensation of rolling shutter effect. IEEE Transactions on Image Processing, 17, 1323–1330.

    Article  PubMed  Google Scholar 

  • Matese, A., Di Gennaro, S. F., Zaldei, A., Genesio, L., & Vaccari, F. P. (2009). A wireless sensor network for precision viticulture: The NAV system. Computers and Electronics in Agriculture, 69, 51–58.

    Article  Google Scholar 

  • Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319–346.

    Article  Google Scholar 

  • Oliver, M. A., & Webster, R. (1990). Kriging: A method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4, 313–332.

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In S. C. Freden & M. A. Becker (Eds.), Third ERTS Symposium (pp. 309–317). Greenbelt, MD: NASA Goddard Space Flight Center.

    Google Scholar 

  • Yang, C., Everitt, J. H., & Bradford, J. M. (2006). Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns. Precision Agriculture, 7, 33–44.

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to Francesco Sabatini (IBIMET—CNR) for his valuable assistance in the platform assembly, to Piero Toscano (IBIMET—CNR) for his knowledge of image processing tools, to Tiziana De Filippis and Leandro Rocchi (IBIMET-CNR) for software development assistance and to all the Mikrokopter staff without whom “VIPtero” would never have left ground. A special thanks goes to Azienda Agricola Comparini for hosting the operational test. This work was supported by a dedicated grant from the Italian Ministry of Economy and Finance to the National Research Council for the project “Innovazione e Sviluppo del Mezzogiorno—Conoscenze Integrate per Sostenibilità ed Innovazione del Made in Italy Agroalimentare—Legge n. 191/2009”.

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Correspondence to Jacopo Primicerio.

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Primicerio, J., Di Gennaro, S.F., Fiorillo, E. et al. A flexible unmanned aerial vehicle for precision agriculture. Precision Agric 13, 517–523 (2012). https://doi.org/10.1007/s11119-012-9257-6

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