An Innovative Methodology to Be More Time-Efficient When Analyzing Data in Precision Viticulture

  • Monica F. RinaldiEmail author
  • Raimondo Gallo
  • Gabriele Daglio
  • Fabrizio Mazzetto
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 67)


Remote Sensing tools in Precision Viticulture to detect disease levels of plants implies big data-sets and time-consuming analysis of data. In this study, we used remote images collected by an Unmanned Aerial Vehicle equipped with a Micasense RedEdge.MXTM multispectral camera and a Terrestrial Laser Scanner (TLS). These tools gave us large amounts of data in a short period. The aim of this research was to develop a methodology that shortens the post-processing phase of data. The data sets were taken from a vineyard during two crop monitoring surveys in June and August 2018. The monitored vineyard is situated in the Piedmont region (Italy). As a first step in the data analysis procedures, we used photogrammetry approaches, as well as the Digital Terrestrial Model and the Digital Surface Model to detect the shape of single plants. The achieved results were then validated with the analysis obtained through the TLS. We then analyzed the reflectance of the canopy using open source software, to detect changes in the pixels about the reflectance curve between healthy plants and plants with disease. We expect that the proposed methodology will help us to be time-efficient and to detect condition of vegetative changes.


Unmanned aerial vehicle (UAV) Terrestrial LIDAR scan (TLS) Multispectral camera 


  1. Gallo, R., Bojeri, Z., Rinaldi, S., & Mazzetto. (2019). Design a web platform to manage environmental monitoring information to be used in Multicriteria evaluations of Green infrastructures. IOP Conference Series: Earth and Environmental Science, 275, 012005 (2019). Web.Google Scholar
  2. Kotovirta, V., Toivanen, T., Tergujeff, R., & Huttunen, M. (2012). Participatory sensing in environmental monitoring: experiences. Palermo, Italy. In Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.Google Scholar
  3. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174 (1977). Web.Google Scholar
  4. Marx, S., Hämmerle, M., Klonner, C., & Höfle, B. (2016). 3D Participatory sensing with low-cost mobile devices for crop height assessment–a comparison with terrestrial laser scanning data. PLoS One, 11(4), e0152839. Published 2016 Apr 13.
  5. Mazzetto, F., Gallo, R., Riedl, M., & Sacco, P. (2019). Proposal of an ontological approach to design and analyses farm information systems to support precision agriculture techniques. IOP Conference Series: Earth and Environmental Science, 275, 012008 (2019). Web.Google Scholar
  6. Riedmiller, M., & Braun, H. (1993). A Direct Adaptative Method for Faster Backpropagation Learning: The RPROP Algorithm. 0-7803-0999-5/93/$03.00©1993 IEEE.Google Scholar
  7. Rosell, & Sanz. (2011). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81.C, 124–141 (2011). Web.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Monica F. Rinaldi
    • 1
    Email author
  • Raimondo Gallo
    • 1
  • Gabriele Daglio
    • 1
  • Fabrizio Mazzetto
    • 1
  1. 1.Faculty of Science and Technology - Fa.S.TFree University of Bozen – BolzanoBolzanoItaly

Personalised recommendations