Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and Satellite Imagery Application

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.

Plant phenotyping relevance

カバー写真とVitiCanopyによるカカオ樹冠構造の取得・評価、およびANNによる樹冠形質からの推定が研究の中心であり、単なる生物学的実験での routine 測定ではない。

Code and data availability

The paper describes canopy image acquisition (VitiCanopy app), satellite imagery processing, and machine learning modelling, but all analysis code is described as unpublished with no public deposit or URL. No phenotype datasets, images, code, or trained models are made publicly available; allowed_urls is empty.

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