A phenomic approach of bamboo species identification using deep learning

Abstract

Abstract Plant has high similarity and dense detail information in morphology, color and texture, especially in bamboo species, which consists of ground tissue and vascular bundles, the cross-sectional images of bamboo belong to fine-grained, for this reason, the classification of bamboo species has always required aid from a domain expert. Recently, deep learning and convolutional neural network (CNN) have become a new solution for image recognition and classification, features can be effectively extracted from bamboo images and high accuracy can be outputted. Here, convolutional neural network models were constructed to achieve the rapid classification of bamboo species, meanwhile, to simulate the complex classification of bamboo, identification complexity was artificially added by mixing all images, but the models were still found feasible. These models are trained to identify 45 bamboo types with Top-1 accuracy of 92.14% and Top-5 accuracy of 98.10%, indicating that the models extracted the specific features from the cross-section images efficiently.