Surveying canopy attributes while conducting fieldwork in the rain forest is
time-consuming. Low-cost imagery such as digital cover photography is a potential
source of information to speed up the process of vegetation assessments
and reduce costs during expeditions. This study presents an imagebased
non-destructive method to estimate canopy attributes of wild cacao
trees in two regions of the rain forest in Colombia, using digital cover photography
and machine learning algorithms. Upward-looking photography at the
base of each cacao tree and machine learning algorithms were used to estimate
gap fraction (GF), foliage cover (FC), crown cover (CC), crown porosity
(CP), clumping index (Ω), and leaf area index (LAI) of the canopy cover. Here
we used the cacao wild trees found on forestry plots as a case study to test
the application of low-cost imagery on the extraction and analysis of canopy
attributes. Canopy attributes were successfully extracted from the canopy
cover imagery and provided 92% of classification accuracy for the structural
attributes of the canopy. Canopy cover attributes allowed us to differentiate
between canopy structures of the Amazon and Pacific rainforests sites suggesting
that wild cacao trees are associated with different vegetation types. We
also compare classification results for the computer extraction of canopy attributes
with a digital canopy cover benchmark. We conclude that our approach
was effective to quickly survey canopy features of vegetation associated
with and of crop wild relatives of cacao. This study allows highly reproducible
estimates of canopy attributes using cover photography and state-ofthe-
art machine learning algorithms such as deep learning Convolutional Neural
Networks.