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

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.