Hybrid deep learning for automated lepidopteran insect image classification

Lepidopterans play an important role in human economy, since some of them are harmful to vegetation in agriculture and some others produce useful materials such as silks, etc. To recognize lepidopteran species correctly is very meaningful to farmers, forest workers, or even insect researchers. This study proposed a cascade architecture which combines the methods of deep convolutional neural network (DCNNs) and Supported Vector Machines (SVMs) to identify Lepidoptera species from their images. The data-set used in this study consists of 1301 Lepidoptera images from 22 species. Since the data-set is not large enough to fine-tune an end-to-end DCNN, we propose a customized solution using part of the DCNN as feature extractor, followed by SVMs as the insect classifiers. The proposed cascade architecture can achieve an accuracy of 100% with our testing data-set and it takes only about 200 ms to recognize insect species from an image, which suggests that the proposed method can be potentially used as a real time classifier for the identification of the Lepidopterans.