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

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.

Classification of Tree Species Based on Point Cloud Projection Images with Depth Information

“…Additionally, we will introduce classification models, such as multi-view CNN [49], which treat multiple views of an object as the same object for classification to avoid the problem of treating different projections of the same object as separate entities. For example, Silva et al [50] achieved 95% accuracy in tree species classification by using microscopic images of three major anatomical parts of wood and combining them with the multi-view random forest model, which is different from the traditional approach of using cross-sectional images alone. In future research, we will explore the optimal combination of multi-view images and the multi-view classification model, along with the point cloud data that are currently in use, to further investigate the upper limit of the classification of tree species using multi-view projection images, which can be obtained quickly and conveniently.…”

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species

“…Previous studies trained microscopic images of the three sections and then identified the test images [ 1 , 34 ]. Here, we explored the impact of different multiples on identification results and feature visualization.…”

“…Illegal logging is the most profitable natural resource crime over the world. The financial value of illegal logging and related trade is approximately $52 to $157 billion per year [ 1 ]. Therefore, the international community has emphasized the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) to ban or restrict trade in endangered tree species to combat illegal logging and related trade [ 2 – 6 ].…”

Plant microphenotype: from innovative imaging to computational analysis

“…In the field of microscopy image analysis, traditional image processing and classical machine learning techniques have been widely employed to process and analyse images with varying signal‐to‐noise ratios and resolutions. Applications include quantifying tissue and cell numbers and hypocotyl sizes (Campbell et al ., 2017 ; Hall et al ., 2016 ), stomata and pavement cell quantification of leaves (Jayakody et al ., 2017 ; Möller et al ., 2017 ), root visualization and quantification (Yamaura et al ., 2022 ), and woody species classification (Rosa da Silva et al ., 2017 , 2022 ). Over the past decade, deep learning has emerged as a rapidly growing technology in plant microscopic imaging.…”