March 3, 2026Open Access

A new era in identification of tick genera; artificial intelligence for precision and speed

Key Points

ResNet50 achieved 100% accuracy in classifying ticks and non-ticks, showcasing exceptional performance based on model evaluation.
VGG16 performed nearly flawlessly with 96.97% accuracy and an AUC score of 99.55% in identifying specific tick genera.
Three distinct AI models contributed to the development of I-TickNet, an application for real-time tick image classification.
The web-based application allows users to identify tick genera efficiently, eliminating the need for specialized knowledge.

Abstract

The performance evaluation and comparison of the model performance has shown that ResNet50 achieved the best result for binary classification of tick and non-tick (experiment A) with accuracy of 100% and Area Under the Curve (AUC) score of 100%. Moreover, VGG16 achieved the best result for binary classification of ticks (experiment B) with an accuracy of 96.97% and AUC score of 99.55% respectively. All the three models were employed for the development of artificial intelligence/Internet of Things (AI/IoT) framework known as I-TickNet for real-time and on-spot classification of tick images. In conclusion, this study provided a web-based application that can identify two distinct tick genera with high accuracy and sensitivity. The application developed enabled a user-friendly interface to identify genera without requiring any expertise.

A new era in identification of tick genera; artificial intelligence for precision and speed

Key Points

Abstract

Cite This Study

Also Consider

Also Consider