A novel method in wood identification based on anatomical image using hybrid model

Abstract

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 process. The 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 logging. With the development of science, wood identification should be supported with technology to enhance the perception of fairness of trade. An 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 model. In the convolutional neural network (CNN), the last layers are usually soft-max functions with dense layers. These layers contain the most parameters that affect the speed model. To 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 developed. Therefore, a hybrid of convolutional neural network and random forest model (CNN-RF model) is introduced to wood identification. The accuracy’s hybrid model is more than 98%, and the processing speed is 3 times higher than the CNN model. The highest accuracy is 1.00 in some species, and the lowest is 0.92. These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images. It also facilitates further investigations of wood cells and has implications for wood science.