Zhan Weihui, Chen Bowen, Wu Xiaolian, Yang Zhen, Lin Che, Lin Jinguo, Guan Xin
College of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
Front Plant Sci. 2023 Jul 7;14:1203836. doi: 10.3389/fpls.2023.1203836. eCollection 2023.
Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex.
A wood species identification model based on wood anatomy and using the genus wood cell geometric dataset was proposed. The model was enhanced by the CTGAN deep learning algorithm and used a simulated cell geometric feature dataset. The machine learning models BPNN and SVM were trained respectively for recognition of three species with simulated vessel cells and simulated wood fiber cells.
The SVM model and BPNN model achieved recognition accuracy of 96.4% and 99.6%, respectively, on the real dataset, using the CTGAN-generated vessel dataset. The BPNN model and SVM model achieved recognition accuracy of 75.5% and 77.9% on real dataset, respectively, using the CTGAN-generated wood fiber dataset.
The machine learning model trained based on the enhanced cell geometric feature data by CTGAN achieved good recognition of , with the SVM model having a higher prediction accuracy than BPNN. The machine learning models were interpreted based on LIME to explore how they identify tree species based on wood cell geometric features. This proposed model can be used for efficient and cost-effective identification of wood species in industrial applications.
在物种层面准确快速地识别木材对于保护和保存树种资源至关重要。当前的识别方法效率低下、成本高昂且复杂。
提出了一种基于木材解剖学并使用属木材细胞几何数据集的木材物种识别模型。该模型通过CTGAN深度学习算法进行增强，并使用模拟细胞几何特征数据集。分别训练机器学习模型BPNN和SVM，以识别具有模拟导管细胞和模拟木纤维细胞的三个物种。
使用CTGAN生成的导管数据集，SVM模型和BPNN模型在真实数据集上的识别准确率分别达到96.4%和99.6%。使用CTGAN生成的木纤维数据集，BPNN模型和SVM模型在真实数据集上的识别准确率分别达到75.5%和77.9%。
基于CTGAN增强的细胞几何特征数据训练的机器学习模型对木材物种具有良好的识别效果，其中SVM模型的预测准确率高于BPNN。基于LIME对机器学习模型进行解释，以探索它们如何基于木材细胞几何特征识别树种。该模型可用于工业应用中木材物种的高效且经济的识别。
