| dc.creator | Mora, Marco | |
| dc.creator | Avila, Felipe | |
| dc.creator | Carrasco-Benavides, Marcos | |
| dc.creator | Maldonado, Gonzalo | |
| dc.creator | Olguín-Cáceres, Jeissy | |
| dc.creator | Fuentes, Sigfredo | |
| dc.date | 2017-10-16T13:40:49Z | |
| dc.date | 2017-10-16T13:40:49Z | |
| dc.date | 2016 | |
| dc.date.accessioned | 2019-11-20T15:08:50Z | |
| dc.date.available | 2019-11-20T15:08:50Z | |
| dc.identifier | http://repositorio.ucm.cl:8080/handle/ucm/567 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3032316 | |
| dc.description | Leaf area index (LAI) is a critical parameter in plant physiology for models related to growth, photosynthetic
activity and evapotranspiration. It is also important for farm management purposes, since it can be
used to assess the vigor of trees within a season with implications in water and fertilizer management.
Among the diverse methodologies to estimate LAI, those based on cover photography are of great interest,
since they are non-destructive, easy to implement, cost effective and have been demonstrated to be
accurate for a range of tree species. However, these methods could have an important source of error in
the LAI estimation due to the inclusion within the analysis of non-leaf material, such as trunks, shoots
and fruits depending on the complexity of canopy architectures. This paper proposes a modified cover
photography method based on specific image segmentation algorithms to exclude contributions from
non-leaf materials in the analysis. Results from the implementation of this new image analysis method
for cherry tree canopies showed a significant improvement in the estimation of LAI compared to ground
truth data using allometric methods and previously available cover photography methods. The proposed
methodological improvement is very simple to implement, with numerical relevance in species with
complex 3D canopies where the woody elements greatly influence the total leaf area. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Computers and Electronics in Agriculture, 123, 195-202 | |
| dc.subject | Leaf area | |
| dc.subject | Image analysis | |
| dc.subject | Hand-held sensor | |
| dc.subject | Canopy cover | |
| dc.title | Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies | |
| dc.type | Artículos de revistas | |
