Estimation of canopy attributes of wild cacao trees using digital cover photography and machine learning algorithms

doi:10.3832/ifor3936-014

Estimation of canopy attributes of wild cacao trees using digital cover photography and machine learning algorithms

Julio Martin Duarte-Carvajalino⁽¹⁾ , Mónica Paramo-Alvarez⁽²⁾, Pablo Fernando Ramos-Calderón⁽³⁾, Carlos Eduardo González-Orozco⁽⁴⁾

iForest - Biogeosciences and Forestry, Volume 14, Issue 6, Pages 517-521 (2021)
doi: https://doi.org/10.3832/ifor3936-014
Published: Nov 17, 2021 - Copyright © 2021 SISEF

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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 image-based 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-of-the-art machine learning algorithms such as deep learning Convolutional Neural Networks.

Keywords

Canopy Attributes, Cover Photography, Colombia, Machine Learning, Deep Learning

Authors’ address

(1)

0000-0001-7117-2051
Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, Centro de Investigación Tibaitatá, Km 14 vía Mosquera, Bogotá, Cundinamarca (Colombia)

(2)

0000-0001-8682-2651
Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, Sede Central, Km 14 vía Mosquera, Bogotá (Colombia)

(3)

0000-0002-0748-8534
Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, Centro de Investigación Nataima, Km 9 vía Espinal-Chicoral, Tolima, Sede Florencia, Caquetá (Colombia)

(4)

0000-0002-4593-9113
Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, Centro de Investigación La Libertad, Km 14 vía Villavicencio, Puerto López, Meta (Colombia)

Corresponding author

Julio Martin Duarte-Carvajalino
jmduarte@agrosavia.co

Citation

Duarte-Carvajalino JM, Paramo-Alvarez M, Ramos-Calderón PF, González-Orozco CE (2021). Estimation of canopy attributes of wild cacao trees using digital cover photography and machine learning algorithms. iForest 14: 517-521. - doi: 10.3832/ifor3936-014

Academic Editor

Nicola Puletti

Paper history

Received: Jul 23, 2021
Accepted: Sep 08, 2021

First online: Nov 17, 2021
Publication Date: Dec 31, 2021
Publication Time: 2.33 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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