Odonata identification using Customized Convolutional Neural Networks

Abstract

The alarming decline of the Odonates (Dragonflies and Damselflies) population needs urgent conservation efforts to balance nature’s food cycle and reduce the negative impact on humans, such as the increasing number of mosquito-borne diseases. The existing odonates identification approaches are not efficient and are mainly based on domain expertise which is highly time-consuming and expensive. The fast-growing Artificial Intelligence technologies have shown impressive solutions to many real-world problems, including wildlife conservation and management. In this study, we proposed the use of Convolutional Neural Networks (CNN) for the identification of odonates. Firstly, we create a large Odonates image dataset comprising 54,176 images belonging to 256 species of odonates. Secondly, we employ nine Customized CNN architectures namely AlexNet, DenseNet121, DenseNet161, DenseNet201, ResNet18, ResNet101, ResNet152, SqueezeNet v1.1, and VGG19 to train our odonates image dataset. Finally, we then compared and empirically studied the performance of our proposed CNN architectures at various image resolutions (224px × 224px, 350px × 350px, 450px × 450px and 550px × 550px). Our pretrained custom DenseNet161 (450px × 450px) performed with highest top-1 accuracy (93.53%), ResNet152 (224px × 224px) with the highest top-3 accuracy (98.07%), DenseNet121 (450px × 450px), and ResNet152 (224px × 224px) with highest top-5 accuracy (98.85%) and DenseNet201 (550px × 550px) with highest F1 score (86.17%). SqueezeNet v1.1 performed the least with highest top-1 accuracy of 83.3%, highest top-3 accuracy (94.24%), highest top-5 accuracy (96.59%), and the highest F1 score (77.68%). The results of our study show that odonates can be reliably and instantly identified using our proposed methodology and the customized model.