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Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN

Author

Listed:
  • Ana Malta

    (RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal)

  • José Lopes

    (Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal)

  • Raúl Salas-González

    (RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    Polytechnic Institute of Coimbra, Coimbra Agriculture School, Bencanta, 3045-601 Coimbra, Portugal)

  • Beatriz Fidalgo

    (RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    Polytechnic Institute of Coimbra, Coimbra Agriculture School, Bencanta, 3045-601 Coimbra, Portugal)

  • Torres Farinha

    (RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal)

  • Mateus Mendes

    (RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
    Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal)

Abstract

Pinus pinaster , commonly called the maritime pine, is a vital species in Mediterranean forests. Its ability to thrive in the local climate and rapid growth make it an essential resource for wood production and reforestation efforts. Accurately estimating the volume of wood within a pine forest is of great significance to the wood industry. The traditional process is either a rough estimation without measurements or a time-consuming process based on manual measurements and calculations. This article presents a method for determining a tree’s diameter, total height, and volume based on a photograph. The method involves placing reference targets of known dimensions on the trees. A deep learning neural network is used to extract the tree trunk and the targets from the background, and the dimensions of the trunk are estimated based on the dimensions of the targets. The results indicate less than 10% estimation errors for diameter, height, and volume in general. The proposed methodology automates the estimation of the dendrometric characteristics of trees, reducing field time consumed in a forest inventory and without the need to use nonprofessional instruments.

Suggested Citation

  • Ana Malta & José Lopes & Raúl Salas-González & Beatriz Fidalgo & Torres Farinha & Mateus Mendes, 2023. "Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16814-:d:1299608
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    References listed on IDEAS

    as
    1. André Guimarães & Maria Valério & Beatriz Fidalgo & Raúl Salas-Gonzalez & Carlos Pereira & Mateus Mendes, 2022. "Cork Oak Production Estimation Using a Mask R-CNN," Energies, MDPI, vol. 15(24), pages 1-15, December.
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