Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)

Abstract

Deep learning algorithms and particularly convolutional neural networks are very successful in pattern recognition from images and are increasingly employed in biology. The development of automated systems for rapid and reliable species identification is vital for insect systematics and may revolutionize this field soon. In this study, we demonstrate the ability of a convolutional neural network to identify species based on habitus photographs with expert-level accuracy in a taxonomically challenging group where a human-based identification would require notorious genitalia dissections. Using the economically important and polymorphic plant bug genus Adelphocoris Reuter (Heteroptera: Miridae) as a model group, we explore the variability in the performance of 11 convolutional neural models most commonly used for image classification, test the role of class-imbalance on the model performance assessment and visualize areas of interest using three interpretation algorithms. Classification performance in our experiments with collection-based habitus photographs is high enough to identify very similar species from a large group with an expert-level accuracy. The accuracy is getting lower only in the experiments with an additional dataset of Adelphocoris and other live plant bugs photographs taken from the Web. Our article demonstrates the importance of comprehensive institutional insect collections for bringing deep learning algorithms into service for systematic entomology using affordable equipment and methods.