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dc.contributor.authorRossi, Riccardo
dc.contributor.authorLeolini, Claudio
dc.contributor.authorCostafreda Aumedes, Sergi
dc.contributor.authorLeolini, Luisa
dc.contributor.authorBindi, Marco
dc.contributor.authorZaldei, Alessandro
dc.contributor.authorMoriondo, Marco
dc.date.accessioned2020-11-10T08:36:23Z
dc.date.available2020-11-10T08:36:23Z
dc.date.issued2020-06-02
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10459.1/69820
dc.description.abstractThis study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.ca_ES
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/s20113150ca_ES
dc.relation.ispartofSensors, 2020, vol. 20, núm. 11, p. 3150ca_ES
dc.rightscc-by (c) Rossi, Riccardo et al., 2020ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject3D phenotypingca_ES
dc.subjectLow-cost platformca_ES
dc.subjectPlant imagingca_ES
dc.subjectStructure for motionca_ES
dc.titlePerformances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotypingca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec030208
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.3390/s20113150


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cc-by (c) Rossi, Riccardo et al., 2020
Except where otherwise noted, this item's license is described as cc-by (c) Rossi, Riccardo et al., 2020