CLASSIFICAÇÃO DE SÍTIO PARA POVOAMENTOS DE EUCALIPTO UTILIZANDO REDES NEURAIS ARTIFICIAIS BASEADA EM VARIÁVEIS AMBIENTAIS E DE MANEJO
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| Formato: | Artículo científico |
| Lenguaje: | pt |
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Universidade Federal de Lavras
2017
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| _version_ | 1876466605592936448 |
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| author | Diogo Nepomuceno Cosenza |
| author_facet | Diogo Nepomuceno Cosenza |
| contents | CLASSIFICAÇÃO DE SÍTIO PARA POVOAMENTOS DE EUCALIPTO UTILIZANDO REDES NEURAIS ARTIFICIAIS BASEADA EM VARIÁVEIS AMBIENTAIS E DE MANEJO Diogo Nepomuceno Cosenza Alvaro Augusto Vieira Soares Aline Edwiges Mazon de Alcântara Antonilmar Araujo Lopes da Silva Rafael Rode Vicente Paulo Soares Helio Garcia Leite Agrociencias Site index Tree plantation Productive capacity Artificial intelligence Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history. 2017 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74452671004 pt http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.3 Vol.23 |
| format | Artículo científico |
| id | redalyc_74452671004 |
| institution | Redalyc |
| language | pt |
| publishDate | 2017 |
| publisher | Universidade Federal de Lavras |
| spellingShingle | CLASSIFICAÇÃO DE SÍTIO PARA POVOAMENTOS DE EUCALIPTO UTILIZANDO REDES NEURAIS ARTIFICIAIS BASEADA EM VARIÁVEIS AMBIENTAIS E DE MANEJO Diogo Nepomuceno Cosenza Agrociencias Site index Tree plantation Productive capacity Artificial intelligence CLASSIFICAÇÃO DE SÍTIO PARA POVOAMENTOS DE EUCALIPTO UTILIZANDO REDES NEURAIS ARTIFICIAIS BASEADA EM VARIÁVEIS AMBIENTAIS E DE MANEJO Diogo Nepomuceno Cosenza Alvaro Augusto Vieira Soares Aline Edwiges Mazon de Alcântara Antonilmar Araujo Lopes da Silva Rafael Rode Vicente Paulo Soares Helio Garcia Leite Agrociencias Site index Tree plantation Productive capacity Artificial intelligence Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history. 2017 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74452671004 pt http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.3 Vol.23 |
| title | CLASSIFICAÇÃO DE SÍTIO PARA POVOAMENTOS DE EUCALIPTO UTILIZANDO REDES NEURAIS ARTIFICIAIS BASEADA EM VARIÁVEIS AMBIENTAIS E DE MANEJO |
| topic | Agrociencias Site index Tree plantation Productive capacity Artificial intelligence |
| url | https://www.redalyc.org/articulo.oa?id=74452671004 |