Animal Intrusion Detection System in Agriculture Using Deep Learning

Efficient detection methods are necessary in the modern era's agriculture as the intrusion of agricultural animals can damage the crops. The authors of this work introduce a Wireless Sensor Network (WSN) and deep learning models, more precisely YOLOv5 image processing modules to improve accuracy in the field for animal entry detection on farms. The solution is built with the scalability and usability of Google Colab, which is an effective platform for building and deploying deep learning models. With the integration of the advanced machine learning model in real-time, detection and classification of intruding animals are executed with perfection using YOLOv5. Thanks to the interaction with the WSN, information about all identified and ongoing events is transferred instantly, thus enabling taking measures within the shortest period of time possible and preventing any further damage. As per the experimental confirmation, the system is claimed to perform quicker and with more precision than classical systems, which is useful for maintenance purposes.