BackgroundThe current state-of-the-art for field wood identification to combat illegal logging relies on experienced practitioners using hand lenses, specialized identification keys, atlases of woods, and field manuals. Accumulation of this expertise is time-consuming and access to training is relatively rare compared to the international demand for field wood identification. A reliable, consistent and cost effective field screening method is necessary for effective global scale enforcement of international treaties such as the Convention on the International Trade in Endagered Species (CITES) or national laws (e.g. the US Lacey Act) governing timber trade and imports.ResultsWe present highly effective computer vision classification models, based on deep convolutional neural networks, trained via transfer learning, to identify the woods of 10 neotropical species in the family Meliaceae, including CITES-listed Swietenia macrophylla, Swietenia mahagoni, Cedrela fissilis, and Cedrela odorata. We build and evaluate models to classify the 10 woods at the species and genus levels, with image-level model accuracy ranging from 87.4 to 97.5%, with the strongest performance by the genus-level model. Misclassified images are attributed to classes consistent with traditional wood anatomical results, and our species-level accuracy greatly exceeds the resolution of traditional wood identification.ConclusionThe end-to-end trained image classifiers that we present discriminate the woods based on digital images of the transverse surface of solid wood blocks, which are surfaces and images that can be prepared and captured in the field. Hence this work represents a strong proof-of-concept for using computer vision and convolutional neural networks to develop practical models for field screening timber and wood products to combat illegal logging.

The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different percentages (missing rates) and according to distinct missing mechanisms, namely, missing completely at random, missing at random, and missing not at random. Since the missing data generation process defines the basis for the imputation experiments (configuration, missing rate, and missing mechanism), it is essential that it is appropriately applied; otherwise, conclusions derived from ill-defined setups may be invalid. The goal of this paper is to review the different approaches to synthetic missing data generation found in the literature and discuss their practical details, elaborating on their strengths and weaknesses. Our analysis revealed that creating missing at random and missing not at random scenarios in datasets comprising qualitative features is the most challenging issue in the related work and, therefore, should be the focus of future work in the field. INDEX TERMS Data preprocessing, missing data, missing data generation, missing data mechanisms.

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The elastic properties of pit membranes are reported to have important implications in understanding air-seeding phenomena in gymnosperms, and pit aspiration plays a large role in wood technological applications such as wood drying and preservative treatment. Here we present force-displacement measurements for pit membranes of circular bordered pits, collected on a mesomechanical testing system. The system consists of a quartz microprobe attached to a microforce sensor that is positioned and advanced with a micromanipulator mounted on an inverted microscope. Membrane displacement is measured from digital image analysis. Unaspirated pits from earlywood of never-dried wood of Larix and Pinus and aspirated pits from earlywood of dried wood of Larix were tested to generate force-displacement curves up to the point of membrane failure. Two failure modes were observed: rupture or tearing of the pit membrane by the microprobe tip, and the stretching of the pit membrane until the torus was forced out of the pit chamber through the pit aperture without rupture, a condition we refer to as torus prolapse.