Deep Feature Extraction for Cymbidium Species Classification Using Global–Local CNN

Abstract

Cymbidium is the most famous and widely distributed type of plant in the Orchidaceae family. It has extremely high ornamental and economic value. With the continuous development of the Cymbidium industry in recent years, it has become increasingly difficult to classify, identify, develop, and utilize orchids. In this study, a classification model GL-CNN based on a convolutional neural network was proposed to solve the problem of Cymbidium classification. First, the image set was expanded by four methods (mirror rotation, salt-and-pepper noise, image sharpening, and random angle flip), and then a cascade fusion strategy was used to fit the multiscale features obtained from the two branches. Comparing the performance of GL-CNN with other four classic models (AlexNet, ResNet50, GoogleNet, and VGG16), the results showed that GL-CNN achieves the highest classification prediction accuracy with a value of 94.13%. This model can effectively detect different species of Cymbidium and provide a reference for the identification of Cymbidium germplasm resources.