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Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard

Author

Listed:
  • Alexandros Sotirios Anifantis

    (Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy)

  • Salvatore Camposeo

    (Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy)

  • Gaetano Alessandro Vivaldi

    (Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy)

  • Francesco Santoro

    (Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy)

  • Simone Pascuzzi

    (Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy)

Abstract

A comparison of three different methods to evaluate the tree row volume (TRV) of a super-high-density olive orchard is presented in this article. The purpose was to validate the suitability of unmanned aerial vehicle (UAV) photogrammetry and 3D modeling techniques with respect to manual and traditional methods of TRV detection. The use of UAV photogrammetry can reduce the amount of estimated biomass and, therefore, reduce the volume of pesticides to be used in the field by means of more accurate prescription maps. The presented comparison of methodologies was performed on an adult super-high-density olive orchard, planted with a density of 1660 trees per hectare. The first method (TRV 1 ) was based on close-range photogrammetry from UAVs, the second (TRV 2 ) was based on manual in situ measurements, and the third (TRV 3 ) was based on a formula from the literature. The comparisons of TRV 2 -TRV 1 and TRV 3 -TRV 1 showed an average value of the difference equal to +13% (max: +65%; min: −11%) and +24% (max: +58%; min: +5%), respectively. The results show that the TRV 1 method has high accuracy in predicting TRV with minor working time expenditure, and the only limitation is that professionally skilled personnel is required.

Suggested Citation

  • Alexandros Sotirios Anifantis & Salvatore Camposeo & Gaetano Alessandro Vivaldi & Francesco Santoro & Simone Pascuzzi, 2019. "Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard," Agriculture, MDPI, vol. 9(11), pages 1-14, October.
    • Handle: RePEc:gam:jagris:v:9:y:2019:i:11:p:233-:d:281519
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    References listed on IDEAS

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    1. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    2. Volodymyr Bulgakov & Simone Pascuzzi & Francesco Santoro & Alexandros Sotirios Anifantis, 2018. "Mathematical Model of the Plane-Parallel Movement of the Self-Propelled Root-Harvesting Machine," Sustainability, MDPI, vol. 10(10), pages 1-11, October.
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    Cited by:

    1. Artur Kraszkiewicz & Artur Przywara & Alexandros Sotirios Anifantis, 2020. "Impact of Ignition Technique on Pollutants Emission during the Combustion of Selected Solid Biofuels," Energies, MDPI, vol. 13(10), pages 1-13, May.
    2. Artur Przywara & Francesco Santoro & Artur Kraszkiewicz & Anna Pecyna & Simone Pascuzzi, 2020. "Experimental Study of Disc Fertilizer Spreader Performance," Agriculture, MDPI, vol. 10(10), pages 1-11, October.
    3. Daniel Queirós da Silva & André Silva Aguiar & Filipe Neves dos Santos & Armando Jorge Sousa & Danilo Rabino & Marcella Biddoccu & Giorgia Bagagiolo & Marco Delmastro, 2021. "Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards," Agriculture, MDPI, vol. 11(3), pages 1-19, March.
    4. Simone Pascuzzi & Alexandros Sotirios Anifantis & Francesco Santoro, 2020. "The Concept of a Compact Profile Agricultural Tractor Suitable for Use on Specialised Tree Crops," Agriculture, MDPI, vol. 10(4), pages 1-10, April.
    5. Volodymyr Bulgakov & Simone Pascuzzi & Semjons Ivanovs & Francesco Santoro & Alexandros Sotirios Anifantis & Ievhen Ihnatiev, 2020. "Performance Assessment of Front-Mounted Beet Topper Machine for Biomass Harvesting," Energies, MDPI, vol. 13(14), pages 1-12, July.
    6. Simone Pascuzzi & Volodymyr Bulgakov & Francesco Santoro & Alexandros Sotirios Anifantis & Semjons Ivanovs & Ivan Holovach, 2020. "A Study on the Drift of Spray Droplets Dipped in Airflows with Different Directions," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    7. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.
    8. Salvatore Camposeo & Gaetano Alessandro Vivaldi & Giovanni Russo & Francesca Maria Melucci, 2022. "Intensification in Olive Growing Reduces Global Warming Potential under Both Integrated and Organic Farming," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    9. Riccardo Lo Bianco & Primo Proietti & Luca Regni & Tiziano Caruso, 2021. "Planting Systems for Modern Olive Growing: Strengths and Weaknesses," Agriculture, MDPI, vol. 11(6), pages 1-18, May.

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