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      A Methodological Proposal for Collecting and Creating Macroscopic Photograph Collections of Tropical Woods with Potential for Use in Deep Learning

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      Biodiversity Information Science and Standards

      Pensoft Publishers

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          Abstract

          Costa Rica is one of the countries with highest species biodiversity density in the world. More than 2,000 tree species have already been identified, many of which are used in the building, furniture, and packaging industries (Grayum et al. 2003). This rich diversity makes the correct identification of tree species very difficult. As a result, it is common to see in the national market that species are commercialized with mistaken identifications, which makes quality control particularly challenging. In addition, because 90 timber tree species have been classified as “threatened” in Costa Rica, correct identifications are indispensable for law-enforcement. The traditional system for tree species identification is based on macro and microscopic evaluations of the anatomy of the wood. It entails assesing anatomical features such as patterns of vessels, parenchymas, and fibers. Typically, 7.7 x 10 cm pieces of wood cuts are used to identify the tree species (Pan and Kudo 2011, Yusof et al. 2013). However, assessing these features is extremely difficult for taxonomists because properties of the wood can vary considerably due to environmental conditions and intra-specific genetic variability. Deep learning techniques have recently been used to identify plant species (Carranza-Rojas et al. 2017a, Carranza-Rojas et al. 2017b) and are potentially useful to detect subtle differences in patterns of vessels, parenchyma, and other anatomical features of wood. However, it is necessary to have a large collection of macroscopic photographs of individuals from various parts of the country (Pan and Kudo 2011). As a first step in the application of deep learning techniques, we have defined a formal, standard protocol for collecting wood samples, physically processing them, taking pictures, performing data augmentation, and using metadata to provide the primary data necessary for deep learning applications. Unlike traditional xylotheque sampling methods that destroy trees or use wood from fallen trees, we propose a method that extracts small size samples with sufficient quality for anatomical characterization but does not affect the growth and survival of the individual. This study has been developed in three forest permanent plots in Costa Rica, all of which are sites with historical growth data over the last 20 years. We have so far evaluated 40 species (10 individuals per species) with diameters greater than 20 cm. From each individual, a cylindrical sample of 12 mm diameter and 7.5 cm in length was extracted with a cordless drill. Each sample is then cut into five of 8 x 8 x 8 mm cubes and further processed to result in curated xylotheque samples, a dataset with all relevant metadata and original images, and a dataset with images obtained by performing data augmentation on the original images.

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          Going deeper in the automated identification of Herbarium specimens

          Background Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. Results In this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data. Conclusions This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.
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            Application of kernel-genetic algorithm as nonlinear feature selection in tropical wood species recognition system

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              Segmentation of pores in wood microscopic images based on mathematical morphology with a variable structuring element

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                Author and article information

                Journal
                Biodiversity Information Science and Standards
                BISS
                Pensoft Publishers
                2535-0897
                March 25 2018
                March 25 2018
                : 2
                : e25260
                Article
                10.3897/biss.2.25260
                © 2018

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