Abstract
In this paper, we describe a systematic approach to the design of an active spectral imaging system for in vivo phenotyping. Our approach takes into account two major factors: spectral sensitivity of the sensor and spectral composition of the illuminant. Similarly to previous works, we adopt a scheme consisting on dimensionality reduction and SVR regression of target chemical parameters from spectral datacubes. We find that high prediction accuracies may be achieved for different sets of parameters depending on the illuminant. Furthermore, in most cases the combination of a single monochromatic illuminant with a dichromatic image sensor (passband and stopband) suffices, which paves the way for the design of tailored low cost imagers. Besides, we demonstrate in vivo estimation of aromatically relevant compounds of white and red grape varieties, not addressed before to our knowledge.
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Acknowledgements.
This work was financed by the Spanish Ministry of Economy and Competitiveness through the Spanish Centre for Technological and Industrial Development CDTI, and the Galician Regional Government, within the FEDER-INNTERCONECTA programme, Galicia 2013 Call (Grant No. ITC-20133114). The authors would also like to thank the wineries Pazo de Señoráns and Señorío de Rubiós for providing the grape samples and SAEC DATA for supporting the regression software development.
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Álvarez-Cid, M.X., García-Díaz, A., Rodríguez-Araújo, J., Asensio-Campazas, A., de la Torre, M.V. (2015). Goal-Driven Phenotyping Through Spectral Imaging for Grape Aromatic Ripeness Assessment. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_31
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DOI: https://doi.org/10.1007/978-3-319-19390-8_31
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