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High-Resolution UAV RGB Imagery Dataset for Precision Agriculture and 3D Photogrammetric Reconstruction Captured over a Pistachio Orchard ( Pistacia vera L.) in Spain

Author

Listed:
  • Sergio Vélez

    (Information Technology Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands)

  • Rubén Vacas

    (Instituto Tecnológico Agrario de Castilla y León (ITACyL), Unidad de Cultivos Leñosos y Hortícolas, 47071 Valladolid, Spain)

  • Hugo Martín

    (Instituto Tecnológico Agrario de Castilla y León (ITACyL), Unidad de Cultivos Leñosos y Hortícolas, 47071 Valladolid, Spain)

  • David Ruano-Rosa

    (Instituto Tecnológico Agrario de Castilla y León (ITACyL), Unidad de Cultivos Leñosos y Hortícolas, 47071 Valladolid, Spain)

  • Sara Álvarez

    (Instituto Tecnológico Agrario de Castilla y León (ITACyL), Unidad de Cultivos Leñosos y Hortícolas, 47071 Valladolid, Spain)

Abstract

A total of 248 UAV RGB images were taken in the summer of 2021 over a representative pistachio orchard in Spain (X: 341450.3, Y: 4589731.8; ETRS89/UTM zone 30N). It is a 2.03 ha plot, planted in 2016 with Pistacia vera L. cv. Kerman grafted on UCB rootstock, with a NE–SW orientation and a 7 × 6 m triangular planting pattern. The ground was kept free of any weeds that could affect image processing. The photos (provided in JPG format) were taken using a UAV DJI Phantom Advance quadcopter in two flight missions: one planned to take nadir images (β = 0°), and another to take oblique images (β = 30°), both at 55 metres above the ground. The aerial platform incorporates a DJI FC6310 RGB camera with a 20 megapixel sensor, a horizontal field of view of 84° and a mechanical shutter. In addition, GCPs (ground control points) were collected. Finally, a high-quality 3D photogrammetric reconstruction process was carried out to generate a 3D point cloud (provided in LAS, LAZ, OBJ and PLY formats), a DEM (digital elevation model) and an orthomosaic (both in TIF format). The interest in using remote sensing in precision agriculture is growing, but the availability of reliable, ready-to-work, downloadable datasets is limited. Therefore, this dataset could be useful for precision agriculture researchers interested in photogrammetric reconstruction who want to evaluate models for orthomosaic and 3D point cloud generation from UAV missions with changing flight parameters, such as camera angle.

Suggested Citation

  • Sergio Vélez & Rubén Vacas & Hugo Martín & David Ruano-Rosa & Sara Álvarez, 2022. "High-Resolution UAV RGB Imagery Dataset for Precision Agriculture and 3D Photogrammetric Reconstruction Captured over a Pistachio Orchard ( Pistacia vera L.) in Spain," Data, MDPI, vol. 7(11), pages 1-11, November.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:157-:d:968354
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    References listed on IDEAS

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    1. Marco Ammoniaci & Simon-Paolo Kartsiotis & Rita Perria & Paolo Storchi, 2021. "State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture," Agriculture, MDPI, vol. 11(3), pages 1-20, February.
    2. Ismail Elkhrachy, 2022. "3D Structure from 2D Dimensional Images Using Structure from Motion Algorithms," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
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    Cited by:

    1. Almasbek Maulit & Aliya Nugumanova & Kurmash Apayev & Yerzhan Baiburin & Maxim Sutula, 2023. "A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan," Data, MDPI, vol. 8(5), pages 1-13, May.

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