Differentiating between morphologically similar species in genus Cinnamomum (Lauraceae) using deep convolutional neural networks

Abstract

Plant adulteration is a rising problem in the forest industry. Cinnamomum osmophloeum Kanehira (Lauraceae) is an evergreen plant that yields cinnamaldehyde. Although two other species—C. burmannii (Nees & T. Nees) Blume and C. insularimontanum Hayata— morphologically resemble C. osmophloeum, they produce a minimum amount of cinnamaldehyde, and thus have lower economical values. The adulteration of C. osmophloeum using C. burmannii has been reported. However, even for experts, differentiating between the three species on the basis of their appearance is challenging due to their high degree of similarity in appearance. This study proposed to identify the three Cinnamomum species using leaf images and deep convolutional neural networks (CNN). In this study, leaf images of the three species were acquired using flatbed scanners. Leaf patches around the blade center of the leaves were extracted. Classifiers based on deep CNN models, VGG16, Inception-V3, and NASNet, were then developed using the leaf images or patches as inputs. Score fusion was then applied to improve the performance of the developed CNN classifiers. The fused CNN classifiers reached a test accuracy as high as 96.7%. A performance comparison indicated that three developed deep CNN classifiers outperformed support vector machine classifiers.