Abstract
This article presents a deep learning approach for classifying the developmental stages (larvae, nymphs, adult females, and adult males) of Ixodes ricinus ticks, the most common tick species in Europe and a major vector of tick-borne pathogens, including Borrelia burgdorferi, Anaplasma phagocytophilum, and tick-borne encephalitis virus (TBEV). Each developmental stage plays a different role in disease transmission, with nymphs considered the most epidemiologically relevant stage due to their small size and high prevalence. We developed a convolutional neural network (CNN) model trained on a dataset of microscopic tick images collected in the area of Upper Silesia, Poland. Grad-CAM, an XAI technique, was used to identify the regions of the image that most influenced the model's decisions. This work is the first to utilize a CNN model for the identification of European tick fauna stages. Compared to existing solutions focused on North American tick species, our model addresses the specific challenge of distinguishing developmental stages within I. ricinus. This solution has the potential to be a valuable tool in entomology, healthcare, and tick-borne disease management.