Temporal Convolutional Network-Based Animal Intrusion Detection Model for Smart Farming

This research aims to develop a reliable model for animal intrusion detection in smart farming by utilizing a temporal convolutional network. The present work has involved three groups. Group 1 refers CNN algorithm which is used for identifying and analysing data. Group 2 refers to Bi-LSTM algorithm is used for training and identifying multi-media data. Group 3 refers to TCN algorithm to improve accuracy and responsiveness. By significantly lowering crop raiding occurrences and improving the ability of intelligent farming to detect animal encroachment, the Integrated Wildlife Management System integrates TCN with computer vision for 98% accuracy. Temporal Convolutional Networks (TCNs) are used by the animal intrusion detection model to improve security and resource management through precise categorization and adaptable handling of changing animal behaviours in agriculture.