Conversion factor estimation of stacked eucalypt timber using supervised image classification with artificial neural networks

Andrade de Barros, Vinicius and Soares, Carlos Pedro Boechat and Fernandes da Silva, Gilson and Casas, Gianmarco Goycochea and Leite, Helio Garcia (2024) Conversion factor estimation of stacked eucalypt timber using supervised image classification with artificial neural networks. Pertanika Journal of Science & Technology (Malaysia), 32 (4). 1527 - 1543. ISSN 2231-8526

Stacked timber is quantified in-store units and then adjusted with a conversion factor for volume estimation in cubic meters, which is important for the wood trade in South America. However, measuring large quantities accurately can be challenging. Digital image processing and artificial intelligence advancements offer promising solutions, making research in this area increasingly attractive. This study aims to estimate conversion factors of stacked Eucalyptus grandis timber using supervised image classification with Artificial Neuronal Network (ANN). Measured data and photographs from an experiment involving thirty stacks of timber were used to achieve this. The conversion factor was determined using photographic methods that involved the applications of equidistant points and ANN and subsequently validated with values observed through the manual method. The ANN method produced more accurate conversion factor estimates than the equidistant points method. Approximately 97% of the ANN estimates were within the ±1% error class, even when using low-resolution digital photographs.

Full text available from: Official URL: http://www.pertanika.upm.edu.my/resources/files/Pe...

Abstract

Stacked timber is quantified in-store units and then adjusted with a conversion factor for volume estimation in cubic meters, which is important for the wood trade in South America. However, measuring large quantities accurately can be challenging. Digital image processing and artificial intelligence advancements offer promising solutions, making research in this area increasingly attractive. This study aims to estimate conversion factors of stacked Eucalyptus grandis timber using supervised image classification with Artificial Neuronal Network (ANN). Measured data and photographs from an experiment involving thirty stacks of timber were used to achieve this. The conversion factor was determined using photographic methods that involved the applications of equidistant points and ANN and subsequently validated with values observed through the manual method. The ANN method produced more accurate conversion factor estimates than the equidistant points method. Approximately 97% of the ANN estimates were within the ±1% error class, even when using low-resolution digital photographs.

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Item Type:	Article
AGROVOC Term:	timberyards
AGROVOC Term:	Eucalyptus
AGROVOC Term:	measurement
AGROVOC Term:	digital image processing
AGROVOC Term:	agricultural innovation
AGROVOC Term:	artificial intelligence
AGROVOC Term:	accuracy
AGROVOC Term:	conversion factors
Geographical Term:	Brazil
Uncontrolled Keywords:	Eucalyptus grandis, forest inventory, forest management, image processing, machine learning
Depositing User:	Ms. Azariah Hashim
Date Deposited:	22 Apr 2026 07:18
Last Modified:	22 Apr 2026 07:18
URI:	http://webagris.upm.edu.my/id/eprint/3007

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