PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

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.

• Aggarwal, C. C. (2018). Neural networks and deep learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-94463-0

• Andrade, M., Peixoto, F. C., & Araújo, A. (1994). Segmentação de imagens através de rede neuronal por satisfação de restrições em ambiente paralelo [Image segmentation using a neural network that fulfils constraints in a parallel environment]. Anais do VII SIBGRAPI, 47-52.

• Bayat, M., Ghorbanpour, M., Zare, R., Jaafari, A., & Pham, B. T. (2019). Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Computers and Electronics in Agriculture, 164, Article 104929. https://doi.org/10.1016/j.compag.2019.104929

• Bertola, A., Soares, C. P. B., Ribeiro, J. C., Leite, H. G., & Souza, A. L. D. (2003). Determination of piling factors through Digitora software. Revista Árvore, 27(6), 837-844. https://doi.org/10.1590/S0100-67622003000600010

• Bueno, G. F., Costa, E. A., Cristina, A. N., Soares, A. A. V., de Miranda, R. O. V., & Schons, C. T. (2020). Effect of number of hidden layer neurons for height-diameter relationship of eucalyptus using artificial neural networks. BIOFIX Scientific Journal, 5(2), 222-230. http://dx.doi.org/10.5380/biofix.v5i2.71374

• Campesato, O. (2020). Artificial Intelligence, Machine Learning, and Deep Learning. Mercury Learning and Information.

• Campos, J. C. C., & Leite, H. G. (2017). Mensuração florestal: Perguntas e respostas (5. ed. atual. e ampl.) [Forest measurement: Questions and answers (5th ed.)]. Universidade Federal de Viçosa.

• Carvalho, A. M., & Camargo, F. R. A. (1996). Avaliacao do metodo de recebimento de madeira por estere [Evaluation of the method of receiving wood by stere]. Revista O Papel, 57, 65-68.

• Casas, G. G., Fardin, L. P., Silva, S., de Oliveira Neto, R. R., Binoti, D. H. B., Leite, R. V., Domiciano, C. A. R., de Sousa Lopes, L. S., da Cruz, J. P., dos Reis, T. L., & Leite, H. G. (2022a). Improving yield projections from early ages in eucalypt plantations with the Clutter model and artificial neural networks. Pertanika Journal of Science & Technology, 30(2). https://doi.org/10.47836/pjst.30.2.22

• Casas, G. G., Gonzáles, D. G. E., Villanueva, J. R. B., Fardin, L. P., & Leite, H. G. (2022b). Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon. Forests, 13(5), Article 697. https://doi.org/10.3390/f13050697

• Cordeiro, M. A., Arce, J. E., Guimarães, F. A. R., Bonete, I. P., Silva, A. V. D. S., Abreu, J. C. D., & Binoti, D. H. B. (2022). Volumetric estimates in eucalyptus stands using support vector machines and artificial neural networks. Madera y bosques, 28(1), Article e2812252. https://doi.org/10.21829/myb.2022.2812252

• Da Cunha Neto, E. M., Bezerra, J. C. F., Veras, H. F. P., Gouveia, D. M., Araujo, E. C. G., & Silva, T. C. (2019). Eucalyptus stem taper estimate through artificial intelligence techniques. BIOFIX Scientific Journal, 4(2), 166-171. http://dx.doi.org/10.5380/biofix.v4i2.65831

• Da Rocha, J. E. C., Junior, M. R. N., Júnior, I. D. S. T., de Souza, J. R. M., Lopes, L., & da Silva, M. L. (2021). Configuration of artificial neural networks for height-diameter relationship of Eucalyptus spp. Scientia Forestalis, 49(132), Article e3706.

• Da Rocha, S. J. S. S., Torres, C. M. M. E., Jacovine, L. A. G., Leite, H. G., Gelcer, E. M., Neves, K. M., Schettini, B. L. S., Villanova, P. H., Silva, L. F. D., Reis, L. P., & Zanuncio, J. C. (2018). Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil. Science of the Total Environment, 645, 655-661. https://doi.org/10.1016/j.scitotenv.2018.07.123

• Da Silva, M. C., Soares, V. P., Pinto, F. D. A. C., Soares, C. P. B., & Ribeiro, C. A. Á. S. (2005). Determination of the volume wooden stacked through processing of digital images. Scientia Forestalis, 69, 104-114.

• De Andrade Sandim, A. S., Mota, A. C., dos Santos, M. L., dos Santos Barros, W., Costa, B. C., & de Andrade, V. M. S. (2019). Stacked volume conversion factor for geometric volume of Eucalyptus sp. Revista Agro@ mbiente On-line, 13, 46-54. https://doi.org/10.18227/1982-8470ragro.v13i0.5312

• De Andrade, V. C. L., Cardoso, A. M., & Binotti, D. H. B. (2022). Growth and yield prognosis of Corymbia citriodora using artificial neural networks. Advances in Forestry Science, 9(2), 1735-1744. https://doi.org/10.34062/afs.v9i2.12829

• De Freitas, E. C. S., de Paiva, H. N., Neves, J. C. L., Marcatti, G. E., & Leite, H. G. (2020). Modeling of eucalyptus productivity with artificial neural networks. Industrial Crops and Products, 146, Article 112149. https://doi.org/10.1016/j.indcrop.2020.112149

• De Miguel-Díez F., Purfürst T., Acuna M., Tolosana-Esteban E., Cremer T. (2023). Estimation of conversion factors for wood stacks in landings and their influencing parameters: a comprehensive literature review for America and Europe. Silva Fennica, 57(1), Article 22018. https://doi.org/10.14214/sf.22018

• De Oliveira Neto, R. R., Leite, H. G., Gleriani, J. M., & Strimbu, B. M. (2022). Estimation of Eucalyptus productivity using efficient artificial neural network. European Journal of Forest Research, 141, 129-151. https://doi.org/10.1007/s10342-021-01431-7

• De Souza, J. R. M., de Oliveira Castro, R. V., Júnior, I. D. S. T., Marcelino, R. A. G., da Silva, R. M., & Moretti, S. D. A. (2023). Stem tapering of eucalyptus spp. using different configurations of artificial neural networks. Floresta, 53(2), 136-144. http://dx.doi.org/10.5380/rf.v53i2.78754

• Ercanlı, İ. (2020). Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height. Forest Ecosystems, 7, Article 12. https://doi.org/10.1186/s40663-020-00226-3

• Gouveia Filho, K. V., Soares, T. S., Cruz, E. S., & Mathias, R. A. M. (2022). Determinação de fatores de empilhamento e volume de madeira empilhada por meio do processamento de imagens digitais [Determination of stacking factors and wood volume stacked through digital image processing]. Advances in Forestry Science, 9(3), 1851-1858. https://doi.org/10.34062/afs.v9i3.13470

• Haykin, S. (2009). Neural networks and learning machines (3rd ed.). Pearson Education India.

• Heinzmann, B., & Barbu, M. C. (2017). Effect of mid-diameter and log-parameters on the conversion factor of cubic measure to solid measure concerning industrial timber. Pro Ligno, 13(1), 39-44.

• Husch, B., Miller, C. I., & Beers, T. W. (1993). Forest mensuration (3rd ed.). Krieger Publishing Company.

• Igel, C., & Hüsken, M. (2003). Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing, 50, 105-123. https://doi.org/10.1016/S0925-2312(01)00700-7

• Kärhä, K., Nurmela, S., Karvonen, H., Kivinen, V. P., Melkas, T., & Nieminen, M. (2019). Estimating the accuracy and time consumption of a mobile machine vision application in measuring timber stacks. Computers and Electronics in Agriculture, 158, 167-182. https://doi.org/10.1016/j.compag.2019.01.040

• Kożuch, A., Cywicka, D., & Adamowicz, K. (2023). A comparison of artificial neural network and time series models for timber price forecasting. Forests, 14(2), Article 177. https://doi.org/10.3390/f14020177

• Mather, P., & Tso, B. (2016). Classification methods for remotely sensed data. CRC Press.

• Meyen, S., & O’Connell, K. (2012). Stacked timber measurement. The 2012 ITGA Forestry & Timber Yearbook. Teagasc Forestry Development Department. https://www.forestry.ie/images/MiscDocs/2012YearbookArticles/StackedTimberMeasurement2012.pdf

• Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Fundamentals of artificial neural networks and deep learning. In Multivariate Statistical Machine Learning Methods for Genomic Prediction (pp. 379-425). Springer. https://doi.org/10.1007/978-3-030-89010-0_10

• Moskalik, T., Tymendorf, Ł., van der Saar, J., & Trzciński, G. (2022). Methods of wood volume determining and its implications for forest transport. Sensors, 22(16), Article 6028. https://doi.org/10.3390/s22166028

• Nylinder, M., Kubénka, T., & Hultnäs, M. (2008). Roundwood measurement of truck loads by laser scanning. A field study at Arauco pulp mill Nueva Aldea.

• Reis, L. P., de Souza, A. L., dos Reis, P. C. M., Mazzei, L., Soares, C. P. B., Torres, C. M. M. E., Da Silva, L. F., Ruschel, A. R., Rêgo, L. J. S., & Leite, H. G. (2018). Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest. Ecological Engineering, 112, 140-147. https://doi.org/10.1016/j.ecoleng.2017.12.014

• Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. IEEE International Conference on Neural Networks, 1, 586-591. https://doi.org/10.1109/ICNN.1993.298623

• Sandoval, S., & Acuña, E. (2022). Stem taper estimation using artificial neural networks for Nothofagus trees in natural forest. Forests, 13(12), Article 2143. https://doi.org/10.3390/f13122143

• Santana, A., Encinas, I., & Muñoz, R. (2023). Stacking factor in transporting firewood produced from a mixture of Caatinga biome species in Brazil. International Journal of Forest Engineering, 34(1), 54-63. https://doi.org/10.1080/14942119.2022.2084674

• Seki, M. (2023). Predicting stem taper using artificial neural network and regression models for Scots pine (Pinus sylvestris L.) in northwestern Türkiye. Scandinavian Journal of Forest Research, 38(1-2), 97-104. https://doi.org/10.1080/02827581.2023.2189297

• Silveira, D. D. P. (2014). Estimation of the volume wooden stacked using digital images and neural networks [Master dissertation]. Universidade Federal de Viçosa, Brazil. http://locus.ufv.br/handle/123456789/3169

• Soares, C. P. B., Ribeiro, J. C., Nascimento Filho, M. B. D., & Ribeiro, J. C. L. (2003). Determination of piling factors through digital photography. Revista Árvore, 27(4), 473-479. https://doi.org/10.1590/S0100-67622003000400007

• Soares, C.B.S., Paula Neto. F., & Souza, A.L. (2011). Dendrometria e inventário florestal (2nd ed.) [Dendrometry and forest inventory (2nd ed.)]. Universidade Federal de Viçosa.

• Tavares Júnior, I. D. S., de Souza, J. R. M., Lopes, L. S. D. S., Fardin, L. P., Casas, G. G., Oliveira Neto, R. R. D., Leite, R. V., & Leite, H. G. (2021). Machine learning and regression models to predict multiple tree stem volumes for teak. Southern Forests: a Journal of Forest Science, 83(4), 294-302. https://doi.org/10.2989/20702620.2021.1994345