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    Rights statement: The final publication is available at Springer via https://doi.org/10.1007/s11119-019-09676-4

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The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees. / Murray, Jon; Fennell, Joseph T. ; Blackburn, Alan et al.
In: Precision Agriculture, Vol. 21, 01.06.2020, p. 473-483.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Murray, J, Fennell, JT, Blackburn, A, Whyatt, D & Li, B 2020, 'The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees', Precision Agriculture, vol. 21, pp. 473-483. https://doi.org/10.1007/s11119-019-09676-4

APA

Murray, J., Fennell, J. T., Blackburn, A., Whyatt, D., & Li, B. (2020). The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees. Precision Agriculture, 21, 473-483. https://doi.org/10.1007/s11119-019-09676-4

Vancouver

Murray J, Fennell JT, Blackburn A, Whyatt D, Li B. The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees. Precision Agriculture. 2020 Jun 1;21:473-483. Epub 2019 Jul 16. doi: 10.1007/s11119-019-09676-4

Author

Murray, Jon ; Fennell, Joseph T. ; Blackburn, Alan et al. / The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees. In: Precision Agriculture. 2020 ; Vol. 21. pp. 473-483.

Bibtex

@article{39687f401cb3415a95a23561beeb7581,
title = "The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees",
abstract = "Within the agrifood sector, the production of high yields is a driver for UK orchard husbandry. Currently, orchard tree management is typically a non-discriminatory method with all trees subjected to the same interventions. Previous studies indicate that structural complexity of individual orchard trees is an indicator for future yield, which can guide the management of individual trees. However, data on the structure of individual trees is often limited. This study investigated the suitability of using remote sensing methods to capture data that can be used to quantify tree structure. Descriptive metrics based on the mathematical assessment of self-affinity and dimensionality were applied to the remotely-sensed data to quantify tree structure, and were also analysed for suitability as a predictor of fruit yield. The findings suggest that while proximal photogrammetry is informative, terrestrial LiDAR data can be used to quantify structural complexity most effectively and this approach holds greater potential for informing orchard management.",
author = "Jon Murray and Fennell, {Joseph T.} and Alan Blackburn and Duncan Whyatt and Bo Li",
note = "The final publication is available at Springer via https://doi.org/10.1007/s11119-019-09676-4 ",
year = "2020",
month = jun,
day = "1",
doi = "10.1007/s11119-019-09676-4",
language = "English",
volume = "21",
pages = "473--483",
journal = "Precision Agriculture",
issn = "1385-2256",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - The Novel Use of Proximal Photogrammetry and Terrestrial LiDAR to Quantify the Structural Complexity of Orchard Trees

AU - Murray, Jon

AU - Fennell, Joseph T.

AU - Blackburn, Alan

AU - Whyatt, Duncan

AU - Li, Bo

N1 - The final publication is available at Springer via https://doi.org/10.1007/s11119-019-09676-4

PY - 2020/6/1

Y1 - 2020/6/1

N2 - Within the agrifood sector, the production of high yields is a driver for UK orchard husbandry. Currently, orchard tree management is typically a non-discriminatory method with all trees subjected to the same interventions. Previous studies indicate that structural complexity of individual orchard trees is an indicator for future yield, which can guide the management of individual trees. However, data on the structure of individual trees is often limited. This study investigated the suitability of using remote sensing methods to capture data that can be used to quantify tree structure. Descriptive metrics based on the mathematical assessment of self-affinity and dimensionality were applied to the remotely-sensed data to quantify tree structure, and were also analysed for suitability as a predictor of fruit yield. The findings suggest that while proximal photogrammetry is informative, terrestrial LiDAR data can be used to quantify structural complexity most effectively and this approach holds greater potential for informing orchard management.

AB - Within the agrifood sector, the production of high yields is a driver for UK orchard husbandry. Currently, orchard tree management is typically a non-discriminatory method with all trees subjected to the same interventions. Previous studies indicate that structural complexity of individual orchard trees is an indicator for future yield, which can guide the management of individual trees. However, data on the structure of individual trees is often limited. This study investigated the suitability of using remote sensing methods to capture data that can be used to quantify tree structure. Descriptive metrics based on the mathematical assessment of self-affinity and dimensionality were applied to the remotely-sensed data to quantify tree structure, and were also analysed for suitability as a predictor of fruit yield. The findings suggest that while proximal photogrammetry is informative, terrestrial LiDAR data can be used to quantify structural complexity most effectively and this approach holds greater potential for informing orchard management.

U2 - 10.1007/s11119-019-09676-4

DO - 10.1007/s11119-019-09676-4

M3 - Journal article

VL - 21

SP - 473

EP - 483

JO - Precision Agriculture

JF - Precision Agriculture

SN - 1385-2256

ER -