# Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning
## 1 Introduction  
Wood is the basic raw material of wood products and the basic form of wood industry products. Wood is also a hard-fibrous structure and is a natural composite material composed of different types of cells and tissues. Different tree species have huge price differences due to their different materials [1]. As the world's largest importer, consumer and exporter of timber and forest products, China is also the world's largest \"illegal timber trading country\" [2]. With the development of the world's economic construction, the demand for wood is increasing in actual work and life day by day, especially rare wood [3, 4, 5]. Therefore, finding an accurate method that can identify wood species, is of great significance to standardize the timber trade market and protecting the interests of all parties, which has also become one of the difficult problems in the research of wood-related technologies. With the improvement of computer digital image processing technology, the idea of object-oriented programming, and the emergence of digital microscope technology, it is now possible to automate the measurement of wood micro-structure parameters, and also became necessary to the study of virtual modeling based on wood micro-structureThe technical means provide a new way for the accurate and rapid identification of wood species at the same time [6]. Deep learning is the most popular research direction in the field of machine learning, which has been rapidly applied in the field of computer vision. Research on classification and recognition of wood species based on deep learning has attracted the attention of scholars at home and abroad [7]. Deep learning-based approaches require large data sets, but this requirement is not easy to satisfy. [PERSON] et al. [8] used the (FSD-M) dataset which consists of 2942 labeled high resolution (3264 \(\times\) 2448) macroscopic images from 41 different wood species of the Brazilian flora. Each category of FSD-M dataset has about 71 images which is relatively low. They applied data augmentation on the input dataset with random flipping to generate more training samples to overcome the limitation of small dataset. [PERSON] et al. [9] used a dataset of 312 wood core images representing 14 European wood species, including both conifer and angiosperm wood. [PERSON] [10] et al. used the same dataset presented in [8]. They also applied data augmentation to reduce overfitting and improve performance. [PERSON] et al. [11] selected 25 wood species for training and testing. The woodblocks of these species collected in two kinds of ways (wood factories in Yunnan Province, China, the others were obtained from the Wood Herbarium of Southwest Forestry University). Then, 120 images for each species were chosen, and over 3000 images for 25 species were obtained. They used transfer learning to minimize the amount and type of data needed in the target area which optimized the model learning content. The implementation of transfer learning can apply domain-specific models to multiple target domains, and it can promote the learning growth of target domain models. [PERSON] et al. [12] used a commercial smartphone equipped with a 14x macro camera lens to capture texture images of 10 North American hardwoods, and constructed a dataset of 1869 images, which identified hardwood species from the macro images using InceptionV4_ResNetV2 convolutional neural network (CNN). [PERSON] et al. [13] obtained 10,237 images from the cross-sections of 417 wood specimens of 15 _Dalbergia_ and 11 _Pterocarpus_ species. [PERSON] et al. [8] proposed a ConvNet improvement-based approach for wood species identification. Due to the small amount of training data, they proposed the addition of dropout layer to ConvNet architecture and data augmentation to increase the size of training data. [PERSON] et al. [9] proposed an automatic identification method for wood species from wood core images with a residual convolutional neural network (CNN).