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Canopy architecture assessment of cherry trees by cover photography based on variable light extinction coefficient modelled using artificial neural networks
| dc.contributor.author | Tongson, E. J. | |
| dc.contributor.author | Fuentes, Sigfredo | |
| dc.contributor.author | Carrasco-Benavides, Marcos | |
| dc.contributor.author | Mora, Marco | |
| dc.date.accessioned | 2019-05-24T20:14:24Z | |
| dc.date.available | 2019-05-24T20:14:24Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/2129 | |
| dc.description.abstract | Leaf area index (LAI) is one of the most important parameters in physiological and functional plant models to estimate tree canopy vigor and photosynthesis. However, LAI requires either destructive or indirect methods for accurate assessment, which can be time consuming, costly, and requires specialized instrumentation. Cover photography to estimate canopy architectural parameters has shown to be effective and accurate for several forest species and horticultural tree crops such as apple trees, grapevines and cherry trees. The accuracy of the LAI estimation is highly dependent on the appropriate use of the variable light extinction coefficient (k) parameter per image. Canopy cover photography was tested on a commercial cherry plantation in Maule, Chile during seasons 2011-12 and 2013-14. Two cultivars were assessed, ‘Bing’ (n=80 images) and ‘Sweet Heart’ (n=80 images), with 10 trees per cultivar, and 4 photos representing each canopy quadrant per tree. Real LAI (LAIreal) was measured allometrically from every tree photographed for both cultivars. Real k was computed based on the inverted LAI formula and LAIreal. Artificial Neural Networks (ANN) modeling for fitting was implemented per cultivar using a customized code written in MATLAB with canopy cover (ff), crown cover (fc), canopy porosity (Φ) and clumping index (Ω) obtained from image analysis algorithms as inputs, and real k as target. The ANN fitting model to obtain a variable k showed determination coefficients (R2) for training = 0.98 and 0.92, validation = 0.96 and 0.94, testing = 0.98 and 0.90, and final k model = 0.98 and 0.94, for ‘Bing’ and ‘Sweetheart’, respectively, in both seasons studied. This resulted in improvements in the LAI estimation for cherry trees when compared to LAIreal with R2 of 0.80 for ‘Bing’ and 0.90 for ‘Sweetheart’. This is a significant improvement in the assessment of canopy vigor and water requirement for tools such as VitiCanopy®, a free LAI estimation App available for iOS and Android devices based on canopy cover photography, which can incorporate a variable k. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | Acta Horticulturae, 1235, 183-188 | es_CL |
| dc.title | Canopy architecture assessment of cherry trees by cover photography based on variable light extinction coefficient modelled using artificial neural networks | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias Agrarias y Forestales | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | www.pubhort.org/actahort/books/1235/1235_24.htm | es_CL |
| dc.ucm.doi | doi.org/10.17660/ActaHortic.2019.1235.24 | es_CL |
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