Methodology for the assessment of leaf area in fruit tree orchards using a terrestrial LiDAR-based system

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dc.contributor.authorLavaquiol Colell, Bernat
dc.contributor.authorLlorens Calveras, Jordi
dc.contributor.authorSanz Cortiella, Ricardo
dc.contributor.authorTorrent Martí, Xavier
dc.contributor.authorPlata Moreno, José Manuel
dc.contributor.authorEscolà i Agustí, Alexandre
dc.date.accessioned2026-06-05T12:06:30Z
dc.date.available2026-06-05T12:06:30Z
dc.date.issued2025-12-01
dc.description.abstractAccurate estimation of canopy geometric and structural characteristics, such as leaf area (LA), is essential for improving resource efficiency in fruit tree crop management. LA is a key biophysical parameter, influencing physiological processes like carbon fixation, evapotranspiration, and light interception, as well as fruit quality and yield. However, its measurement is complex due to the substantial number of leaves and the three-dimensional nature of tree canopies.An alternative approach, the Projected Tree Row Surface (PTRS), has shown a strong correlation with LA and has been recognized by the scientific community. Despite its robustness, the original PTRS method requires time-consuming manual data collection, which limits its practical application in the field.This study introduces a novel automated methodology for calculating the PTRS, validated using high-resolution ground-truth data providing LA values at 0.1-m intervals along the tree rows. When evaluated on almond, pear, and apple trees as well as vineyards, the method achieved remarkably high correlations between PTRS and LA, with coefficients up to r = 0.97 and r = 0.99 at optimal resolutions (0.1 –0.2 m PTRS per 1 m row section). These results demonstrate that the approach delivers consistent and reliable measurements of LA under diverse field conditions, enabling real-time, high-resolution assessment of tree-row canopies.The automated PTRSn approach enables fast and efficient LA estimation and can be adapted to any point cloud dataset. It supports flexible resolution to balance accuracy and processing time and can be applied to full rows, individual trees, or canopy segments. This methodology represents a step forward in automating LA assessment and supports the development of real-time applications in precision agriculture.
dc.description.sponsorshipThe present study is part of the PAgPROTECT project PID2021-PID2021-126648OB-I00 funded by MICIU/AEI/https://doi.org/10.13039/501100011033 and by FEDER/ERDF, EU. The authors wish to thank IRTA for allowing the trial to be conducted at their experimental station in Mollerussa and the company AlmondFoods for permitting the experimentation to be conducted on their commercial farm.
dc.identifier10.1007/s11119-025-10296-4
dc.identifier.doihttps://doi.org/10.1007/s11119-025-10296-4
dc.identifier.issn13852256
dc.identifier.urihttps://hdl.handle.net/10459.1/470430
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation126648OB
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1007/s11119-025-10296-4
dc.relation.ispartofPrecision Agriculture, 2025, vol. 26, núm. 96, p. 1-27
dc.relation.ispartofseriesPrecision Agriculture
dc.rightscc-by-nc-nd (c) Lavaquiol et al., 2026
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLeaf area (LA)
dc.subjectProjected tree row surface (PTRS)
dc.subjectAutomatization
dc.subjectLiDAR
dc.subjectPrecision agriculture
dc.titleMethodology for the assessment of leaf area in fruit tree orchards using a terrestrial LiDAR-based system
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
oaire.citation.issue6
oaire.citation.volume26
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