ABSTRACT:

In modern agriculture, protecting crops and livestock from animal intrusion is essential to minimize damage and losses. This project presents an AI-driven animal detection system that utilizes a custom YOLO (You Only Look Once) model to detect various animals in real time. The system processes video feed or images, identifies the presence of specific animals, and triggers a corresponding sound alert based on the detected species. By integrating deep learning-based object detection with automated sound responses, this system enhances farm security, reduces manual monitoring efforts, and provides an efficient solution for mitigating wildlife-related agricultural threats. The model is trained on a dataset containing various animal species commonly found near agricultural fields, ensuring high detection accuracy.

INTRODUCTION:

Agriculture has been the backbone of human civilization, providing food, raw materials, and economic stability for centuries. However, one of the major challenges faced by farmers is the intrusion of animals into farmlands, which can lead to severe crop damage, loss of yield, and disruption of farming activities. Wild animals such as deer, wild boars, monkeys, and birds often invade agricultural fields in search of food, while stray animals like cows, goats, and dogs can also enter cultivated lands, trampling crops and affecting farm productivity. These intrusions result in financial losses, reduced agricultural output, and increased labor costs for farmers who must constantly monitor their fields to prevent such occurrences. Traditional methods of preventing animal intrusions, such as scarecrows, fencing, manual surveillance, and chemical repellents, have limited effectiveness and often require continuous human intervention. Moreover, these conventional techniques do not provide an automated solution for real-time animal detection and deterrence, making them inefficient for large-scale farming operations.

With the advancement of artificial intelligence (AI) and machine learning (ML), computer vision-based animal detection has emerged as an innovative and highly effective solution for this problem. This project focuses on developing an intelligent animal detection system using a custom YOLO (You Only Look Once) model, a powerful deep learning-based object detection algorithm. The system is designed to analyze live video feeds or images, accurately identify various animals in real time, and automatically trigger a corresponding sound alert based on the detected species. These sound alerts serve two primary purposes: deterring harmful animals that pose a threat to crops and alerting farmers about the presence of intruding animals, allowing them to take necessary action.
The proposed system utilizes machine learning techniques to train the YOLO model on a dataset containing images of various animals commonly found near agricultural lands. The model learns to recognize these animals with high precision, ensuring reliable and quick identification. The use of a real-time detection system eliminates the need for constant human monitoring, thereby reducing labor costs and increasing efficiency. Additionally, the sound-based alert mechanism provides an eco-friendly and non-invasive approach to keeping unwanted animals away from farmlands, unlike traditional methods such as electric fencing or harmful chemical deterrents.
This AI-driven approach to animal detection and deterrence in agriculture has the potential to enhance farm security, improve productivity, and minimize losses due to wildlife intrusion. By integrating deep learning, computer vision, and automated response mechanisms, this system offers a scalable and adaptable solution for modern farmers, ensuring better crop protection, improved farm management, and higher agricultural yields. As agriculture continues to evolve with technological advancements, such intelligent systems will play a crucial role in making farming more efficient, sustainable, and resilient against external threats.