부산대학교 도서관

Animal intrusion is a significant challenge for farmers, causing extensive crop damage, human injuries, and substantial financial losses. Traditional animal movement monitoring and surveillance methods alone are insufficient to provide a permanent solution. To solve this problem, a novel system architecture that combines the latest convolutional neural network (CNN) algorithm and Internet of Things (IoT) technology is proposed. Our proposed system architecture integrates various components, including a Raspberry Pi as a central processing unit, cloud storage for efficient data management, and a GSM module for instant alert generation. To train the CNN algorithm, a comprehensive and diverse Animal Dataset consisting of various animal species commonly found in farmland areas is curated. The dataset encompasses a wide range of annotated images, enabling the CNN algorithm to accurately identify and classify animals. The Raspberry Pi serves as the core of the system, responsible for real-time image processing and analysis. Utilizing the power of the CNN algorithm, the Raspberry Pi processes the captured images from strategically placed surveillance cameras. When an animal intrusion is detected, the system promptly generates an alert via the integrated GSM module, providing immediate notifications to farmers and relevant authorities. Furthermore, the system leverages cloud storage to store and manage the collected data, facilitating easy access and retrieval for analysis and system improvement. This cloud-based approach enables scalability, allowing the system to handle large amounts of data efficiently. By integrating the CNN algorithm, IoT, Raspberry Pi, cloud storage, and GSM module, a comprehensive and robust framework for real-time detection and prevention of animal intrusion is provided. The system's ability to swiftly identify and alert farmers and authorities about potential threats minimizes crop damage, ensures human safety, and significantly reduces financial losses.