## 5 Conclusions  
The data analysis tools presented in this paper could contribute significantly to adapting management strategies from growers, specifically in relation to canopy management, to obtain desired aroma profiles at the tree-by-tree scale. Specific practical applications could be related to homogenize the aroma quality profile within plants and the entire plantation or to increase desired and specific aromas to change the style of the cocoa volatiles. These management strategies can be implemented within the critical stages of the growing period of cocoa trees, or close to harvest. By implementing VitiCanopy and the machine learning modeling strategies presented, these potential changes in the volatile composition of beans related to canopy architecture changes due to canopy management can be obtained in real time at the fingertips of growers through their portable electronic devices (smartphones and tablet PCs). The latter will enhance crop management capabilities from major industries, but more importantly, by growers in developing countries.  
The following are available online at [[http://www.mdpi.com/1424-8220/19/14/3054/s1](http://www.mdpi.com/1424-8220/19/14/3054/s1)]([http://www.mdpi.com/1424-8220/19/14/3054/s1](http://www.mdpi.com/1424-8220/19/14/3054/s1)), Figure S1. Map showing the site location where the study was conducted. The image was obtained from Google Earth (Google Technology company, Menlo Park, CA, USA); Figure S2. Diagram representing the methods used in the study, where (A) represents the canopy architecture assessment at ground level using the VitiCanopy app, (B) depicts the cacao pods processing and assessment of volatile compounds using gas chromatography, mass spectroscopy, (C) the development of the machine learning modelling using (A) as inputs and (B) as targets/outputs, (D) represents the canopy architecture assessment from aerial images using a customized Matlab(r) code, and (E) the development of spatial distribution maps using the outputs from (C) using the data from (D) as inputs; Figure S3. Peak areas of the six volatile compounds found in the fermented cocoa bean samples used to develop the machine learning model. All values are reported in scientific notation \(10^{3}\). Sample codes: B1 = Block 1, B2 = Block 2, C# = number of the tree, and A or B = position of the tree (row).  
Conceptualization, S.F.; data curation, S.F. and C.G.V.; formal analysis, S.F. and C.G.V.; investigation, S.F., G.C., D.D.T., and A.Z.; methodology, S.F., G.C., D.D.T., A.Z., and C.G.V.; project administration, S.F.; software, S.F.; supervision, S.F.; validation, C.G.V.; visualization, S.F. and C.G.V.; writing--original draft, S.F., G.C., D.D.T., A.Z., and C.G.V.; writing--review & editing, S.F. and C.G.V.  
This research was partially funded by the Ecuadorian government through the academic-award scholarships program granted to GC. This research was supported by the Digital Viticulture program funded by the University of Melbourne's Networked Society Institute, Australia.  
The authors would like to acknowledge the support from [PERSON], Principal Horticultural from the Department of Agriculture and Fisheries, Queensland, Australia. Furthermore, the authors acknowledge [PERSON] for his support in the use of GC-MS in the chemistry laboratory of The University of Melbourne, Australia.  
The authors declare no conflict of interest.