Advances in species recognition technologies can contribute to the conservation and protection of flora species, especially those threatened with extinction. The aim of this research was to compare the early fusion approaches of operators known as Local Binary Patterns (LBP) and late fusion, carried out at the level of the decision classifiers, in the construction of an automatic recognition system of forest species. 1901 macroscopic images of wood from 46 Brazilian species were used. The extraction of image characteristics was done using two variants of the LBP descriptor, covering different aspects of spatial and angular resolution. The repeated stratified k-fold cross-validation method was used to estimate the performance of the classifiers. The cross-validation folds were created using stratified random sampling, whose strata were the prediction classes. An automatic recognition system based on the concatenation of rotation-invariant LBP histograms and the SVM classifier showed an F1-score of 97.67%. The fusion of classifiers, through majority voting, improved the F1-score of this system by 0.33% point. This experiment revealed that more than 50% of the species showed no misclassification or occurred only once or twice. It was identified that some groups of species generally confused by wood anatomists were perfectly differentiated by this classification system. The recognition system showed good ability to identify species, and if this technology is combined with traditional identification tools and empirical knowledge, it is possible to minimize errors in the identification of Brazilian flora, especially endangered species, for which the proposed classification system showed high accuracy.

中文翻译：

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基于木材宏观图像纹理特征的巴西植物区系自动识别系统

物种识别技术的进步有助于保护和保护植物群物种，尤其是那些濒临灭绝的物种。本研究的目的是比较在决策分类器级别执行的称为局部二元模式 (LBP) 和后期融合的算子的早期融合方法，以构建森林物种的自动识别系统。1901 年使用了 46 种巴西木材的宏观图像。图像特征的提取是使用 LBP 描述符的两个变体完成的，涵盖空间和角分辨率的不同方面。使用重复分层 k 折交叉验证方法来估计分类器的性能。交叉验证折叠是使用分层随机抽样创建的，其层是预测类。基于旋转不变 LBP 直方图和 SVM 分类器串联的自动识别系统显示 F1 分数为 97.67%。分类器的融合，通过多数投票，使该系统的 F1-score 提高了 0.33 个百分点。该实验表明，超过 50% 的物种没有出现错误分类或仅发生一两次。已经确定，一些通常被木材解剖学家混淆的物种组被这个分类系统完全区分了。该识别系统显示出良好的物种识别能力，如果将该技术与传统的识别工具和经验知识相结合，可以最大限度地减少巴西植物群，尤其是濒危物种的识别错误，