PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application
Version 1
: Received: 26 April 2019 / Approved: 28 April 2019 / Online: 28 April 2019 (11:36:14 CEST)
How to cite:
Fuentes, S.; Chacon, G.; Torrico, D.D.; Zarate, A.; Gonzalez Viejo, C. Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application. Preprints2019, 2019040316. https://doi.org/10.20944/preprints201904.0316.v1
Fuentes, S.; Chacon, G.; Torrico, D.D.; Zarate, A.; Gonzalez Viejo, C. Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application. Preprints 2019, 2019040316. https://doi.org/10.20944/preprints201904.0316.v1
Fuentes, S.; Chacon, G.; Torrico, D.D.; Zarate, A.; Gonzalez Viejo, C. Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application. Preprints2019, 2019040316. https://doi.org/10.20944/preprints201904.0316.v1
APA Style
Fuentes, S., Chacon, G., Torrico, D.D., Zarate, A., & Gonzalez Viejo, C. (2019). Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application. Preprints. https://doi.org/10.20944/preprints201904.0316.v1
Chicago/Turabian Style
Fuentes, S., Andrea Zarate and Claudia Gonzalez Viejo. 2019 "Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application" Preprints. https://doi.org/10.20944/preprints201904.0316.v1
Abstract
Cocoa is an important commodity crop not only to produce one of the most complex products such as chocolate from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried and grinded to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm and the aroma profile considering six main aromas as targets. The ANN model rendered high accuracy (R = 0.82; MSE = 0.09) with no overfitting. The model was then applied to a satellite image from the whole cocoa field studied to produce canopy vigor and aroma profile maps up to the tree-by-tree scale. The tool developed could aid significantly the canopy management practices in cocoa trees that have a direct effect on cocoa quality.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
