Improved wood species identification based on multi-view imagery of the three anatomical planes

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da Silva, Nubia Rosa ; Deklerck, Victor ; Baetens, Jan M. ; Van den Bulcke, Jan ; De Ridder, Maaike ; Rousseau, Melissa ; Bruno, Odemir Martinez ; Beeckman, Hans ; Van Acker, Joris ; De Baets, Bernard ; Verwaeren, Jan

• Tipo de documento: Artigo Científico
• Fonte: PLANT METHODS; v. 18, n. 1, p. 17-pg., 2022-06-11.

Resumo

Background The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance. (AU)

• Processo FAPESP: 14/06208-5 - Análise de imagem de lignocelulose
• Beneficiário: Núbia Rosa da Silva
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
• Processo FAPESP: 11/21467-9 - Reconhecimento de Padrões Heterogêneos e suas Aplicações em Biologia e Nanotecnologia.
• Beneficiário: Núbia Rosa da Silva
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 11/01523-1 - Métodos de visão computacional aplicados à identificação e análise de plantas
• Beneficiário: Odemir Martinez Bruno
• Modalidade de apoio: Auxílio à Pesquisa - Regular