Mobile terrestrial laser scanner applications in precision fruticulture/ horticulture and tools to extract information from canopy point clouds

Escolà i Agustí, Alexandre; Martínez Casasnovas, José Antonio; Rufat i Lamarca, Josep; Arnó Satorra, Jaume; Arbonés, Amadeu; Sebé Feixas, Francesc; Pascual Roca, Miquel; Gregorio López, Eduard; Rosell Polo, Joan Ramon

doi:https://doi.org/10.1007/s11119-016-9474-5

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Issue date

2017-01-01

Author

Escolà i Agustí, Alexandre

Martínez Casasnovas, José Antonio

Rufat i Lamarca, Josep

Arnó Satorra, Jaume

Arbonés, Amadeu

Sebé Feixas, Francesc

Pascual Roca, Miquel

Gregorio López, Eduard

Rosell Polo, Joan Ramon

Suggested citation

Escolà i Agustí, Alexandre; Martínez Casasnovas, José Antonio; Rufat i Lamarca, Josep; Arnó Satorra, Jaume; Arbonés, Amadeu; Sebé Feixas, Francesc; ... Rosell Polo, Joan Ramon. (2017) . Mobile terrestrial laser scanner applications in precision fruticulture/ horticulture and tools to extract information from canopy point clouds. Precision Agriculture, 2017, vol. 18, núm.1, p. 111-132. https://doi.org/10.1007/s11119-016-9474-5.

Abstract

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed

to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.

URI

http://hdl.handle.net/10459.1/62753

DOI

https://doi.org/10.1007/s11119-016-9474-5

Is part of

Precision Agriculture, 2017, vol. 18, núm.1, p. 111-132