A Novel Method in Wood Identification Based on Anatomical Image Using Hybrid Model

作     者：Nguyen Minh Trieu Nguyen Truong Thinh 

作者机构：College of Technology and DesignUniversity of Economics Ho Chi Minh City—UEHHo Chi Minh City72516Vietnam 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第11期

页      面：2381-2396页

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Đại học Kinh tế Thành phố Hồ Chí Minh 

主　　题：Identifying wood anatomical wood hybrid model CNN-RF automatic identification vietnam wood 

摘      要：Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training *** quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal *** the development of science,wood identification should be supported with technology to enhance the perception of fairness of *** automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed *** the convolutional neural network(CNN),the last layers are usually soft-max functions with dense *** layers contain the most parameters that affect the speed *** reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and ***,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood *** accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN *** highest accuracy is 1.00 in some species,and the lowest is *** results show the excellent adaptability of the hybrid model in wood identification based on anatomical *** also facilitates further investigations of wood cells and has implications for wood science.