Periodic Convolutional Neural Network-Based Approach for Detecting Wildlife Penetration in Smart Agriculture

The combination of habitat encroachment and deforestation events produces escalating incidents of wild animals raiding crops that results in significant farmer revenue loss with additional dangers to human populations. Through the usage of computer vision implementing Temporal Convolutional Networks (TCN) to accurately recognize animals and categorize their species combined with the direct use of ultrasound emission techniques that repel specie-specific animals, this project is going to create a new Integrated Wildlife Management System. The system operates through edge computing to provide instantaneous handling of crop raiding situations. When the edge computing device initiates camera activation the system deploys its advanced Animal Intrusion Detection Model. Once the problematic species have been identified via this framework, the detection system activates the Wildlife Dispelling Unit through activation triggers. The unit ultimately emits species-specific ultrasonic signals when required to deter invasive wildlife to prevent further damage. Unlike conventional ways our system both preserves environmental cleanliness and manages financial needs through maintenance and reliability issues. The Integrated Wildlife Management System implements leading-edge technology to create a versatile paradigm that defends crops against elephant’s wild boar and deer and other animals. Through technological advancements this project enhances current human-wildlife resolution struggles while exhibiting technological applications that establish mutual coexistence between farmers and nature.