Figueroa-Mata Geovanni, Mata-Montero Erick, Valverde-Otárola Juan Carlos, Arias-Aguilar Dagoberto, Zamora-Villalobos Nelson
School of Mathematics, Costa Rica Institute of Technology, Cartago, Costa Rica.
School of Computing, Costa Rica Institute of Technology, Cartago, Costa Rica.
Front Plant Sci. 2022 Apr 1;13:789227. doi: 10.3389/fpls.2022.789227. eCollection 2022.
Tree species identification is critical to support their conservation, sustainable management and, particularly, the fight against illegal logging. Therefore, it is very important to develop fast and accurate identification systems even for non-experts. In this research we have achieved three main results. First, we developed-from scratch and using new sample collecting and processing protocols-an dataset called that comprises macroscopic cross-section images of 147 native tree species from Costa Rica. Secondly, we implemented a CNN for automated tree species identification based on macroscopic images of cross-sections of wood. For this CNN we apply the fine-tuning technique with VGG16 as a base model, pre-trained with the ImageNet data set. This model is trained and tested with a subset of 75 species from CRTreeCuts. The top-1 and top-3 accuracies achieved in the testing phase are 70.5% and 80.3%, respectively. The Same-Specimen-Picture Bias (SSPB), which is known to erroneously increase accuracy, is absent in all experiments. Finally, the third result is Cocobolo, an Android mobile application that uses the developed CNN as back-end to identify Costa Rican tree species from images of cross-sections of wood.
树种识别对于支持其保护、可持续管理，尤其是打击非法采伐至关重要。因此，即使对于非专业人员而言，开发快速且准确的识别系统也非常重要。在本研究中，我们取得了三个主要成果。首先，我们从零开始并使用新的样本采集和处理协议，开发了一个名为CRTreeCuts的数据集，该数据集包含来自哥斯达黎加的147种本地树种的宏观横截面图像。其次，我们基于木材横截面的宏观图像实现了一个用于自动树种识别的卷积神经网络（CNN）。对于这个CNN，我们应用微调技术，以在ImageNet数据集上预训练的VGG16作为基础模型。该模型使用CRTreeCuts中的75个物种的子集进行训练和测试。在测试阶段实现的top-1和top-3准确率分别为70.5%和80.3%。在所有实验中均不存在已知会错误提高准确率的同一样本图片偏差（SSPB）。最后，第三个成果是Cocobolo，这是一款安卓移动应用程序，它使用所开发的CNN作为后端，从木材横截面图像中识别哥斯达黎加的树种。
