# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 1 Introduction
### Literature Review  
In recent years, multiple studies have presented IoT-based farm monitoring systems, with an emphasis on technology such as PIR sensors, ultrasonic detection, and deep learning models for animal identification. However, few systems have attained the precision and real-time detection required for large-scale agricultural monitoring. Table 1 represents the summary of the research contributions made in this domain. \begin{table}
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Recent improvements in agricultural security frameworks have utilized numerous technologies to improve the safeguarding of farming activities. One study [4] presents an advanced system that employs passive infrared (PIR) and ultrasonic sensors in conjunction with a Raspberry Pi microprocessor. This system efficiently identifies and classifies intruders based on their physical attributes, providing prompt notifications via email, GSM modules, and aural alarms. Furthermore, it integrates a camera to photograph possible intruders, thus furnishing farmers with immediate information about hazards. This method effectively detects a wide array of intrusions, including both fauna and people. Another study presents a unique method that utilizes computer vision and artificial intelligence (AI) technology for animal identification and warning systems [5]. This system utilizes strategically placed cameras to oversee weak entry points and processes the visual data using OpenCV and a pretrained MobileNet SSD model. Upon recognizing an animal, the system initiates an audio alarm and disseminates warnings to the agricultural community, while simultaneously preserving a thorough register of all recognized species. This AI-driven monitoring not only boosts crop security but also assists in decreasing possible losses. Paper [6] delineates a comparable agricultural security framework that incorporates PIR sensors, ultrasonic sensors, and a Raspberry Pi microcontroller to enhance farm security. This system includes four PIR sensors positioned at varied heights to detect motion and categorize intruders. Upon detection of an incursion, a camera is activated, and notifications are dispatched by email and through GSM modules. An aural alert serves to dissuade the intruder, creating a cost-effective solution to agricultural security concerns and delivering real-time warnings with minimum human interaction. Moreover, another study specifies a novel apparatus for animal identification and agricultural protection that involves an array of sophisticated sensors, microcontrollers, and IoT technologies [7]. This system employs PIR sensors to monitor animal movements, while auxiliary sensors, including Light-Dependent Resistors (LDR) and Flame Sensors, provide functionalities such as automated lighting management and forest fire detection. When animal activity is identified, the system activates acoustic signals, lights the area, and sends messages to both the farming community and forest authorities via an implanted \begin{table}
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**Authors** & **Approach** & **Features** & **Contribution** \\ \hline [PERSON] & & & Discusses drone \\ Panjaitan & & & implementation in paddy \\ et al., 2022 & Drones for spraying & UAVs, precision agriculture & fields, assesses drone \\  & & & performance, and highlights \\  & & & the impact of drone-based \\  & & & spraying. \\ \hline [PERSON] & & & Focuses on LoRa/LoRaWAN \\ et al., 2022 & LoRa/LoRaWAN & IoT, LoRaWAN, wireless & technology in agriculture, \\
[23] & in agriculture & & addressing challenges and \\  & & & wireless \\
[23] & & connectivity & Highlights the role of \\  & & & machine learning, \\  & & & AI, and edge computing. \\ \hline [PERSON] et al., & Seed viability & X-ray imaging, & \\
2023 [24] & prediction & deep learning & \\  & & & using X-ray imaging. Achieves \\  & & & high accuracy in distinguishing \\  & & & between viable and non-viable seeds. \\ \hline \end{tabular}
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Table 1: _Cont._WiFi module.