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

Author(s):Show less -

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

• Document type: Journal article
• Source: PLANT METHODS; v. 18, n. 1, p. 17-pg., 2022-06-11.

Abstract

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)

• FAPESP's process: 14/06208-5 - Image analysis of lignocellulosics
• Grantee: Núbia Rosa da Silva
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 11/21467-9 - Heterogeneous Pattern Recognition and its Applications in Biology and Nanotechnology.
• Grantee: Núbia Rosa da Silva
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 11/01523-1 - Computer vision methods applied to the identification and analysis of plants
• Grantee: Odemir Martinez Bruno
• Support Opportunities: Regular Research Grants