AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

Abstract

AInsectID Version 1.1 is a Graphical User Interface (GUI)‐operable open‐source insect species identification, color processing, and image analysis software. The software has a current database of 150 insects and integrates artificial intelligence approaches to streamline the process of species identification, with a focus on addressing the prediction challenges posed by insect mimics. This paper presents the methods of algorithmic development, coupled to rigorous machine training used to enable high levels of validation accuracy. Our work integrates the transfer learning of prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, and ResNet101. Here, we employ both fine tuning and hyperparameter optimization approaches to improve prediction performance. After extensive computational experimentation, ResNet101 is evidenced as being the most effective CNN model, achieving a validation accuracy of 99.65%. The dataset utilized for training AInsectID is sourced from the National Museum of Scotland, the Natural History Museum London, and open source insect species datasets from Zenodo (CERN's Data Center), ensuring a diverse and comprehensive collection of insect species.