# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 1 Introduction
### Literature Review
Night vision cameras boost its monitoring capabilities during dark, offering an effective deterrent to preserve crops without injuring wildlife. For further advancement of agricultural production globally, the potential for automation in important areas like temperature control remains underexplored. An eco-friendly, cost-effective solution leveraging the IoT intends to boost accessibility in Indian farming by providing real-time data on water management, temperature, electricity, and lighting. Recent studies of the performance of drones in rice fields indicate the efficiency of agricultural spraying methods [22]. As the world population continues to expand, the integration of smart agriculture powered by IoT and LoRaWAN technologies is regarded as crucial for tackling agricultural concerns. IoT sensors are crucial in monitoring resource consumption and crop health, with LoRaWAN providing environmental evaluations, irrigation control, and animal tracking. In study [23], IoT sensors were used to gather information on resource utilization and crop conditions to enhance farming. This move relies significantly on LoRaWAN, which offers environmental monitoring, irrigation control, and animal tracking. Moreover, its unique technique, applying X-ray imaging paired with deep learning, attained an accuracy rate of 86.01% in differentiating between viable and non-viable seeds [24]. Improvements in animal identification were investigated through the application of Weighted Co-occurrence Histograms of Oriented Gradients (W-CoHOG) for spotting animal incursions in agricultural settings [8]. This method employs thorough studies of farm-captured imagery, boosting its detection skills by incorporating gradient direction and magnitude. The adoption of a sliding window approach improves detection across varied animal sizes and camera focus lengths. Another important paper describes a system for identifying animal invasions in rice fields and other agricultural settings via sophisticated image processing techniques [9]. This complex procedure comprises gathering pictures via security cameras, followed by background removal, object segmentation, and object recognition, and then employing the Scale-Invariant Feature Transform (SIFT). Once an animal is discovered, an alarm message is used to inform farmers. In a separate context, the DeepAnomaly technique [10], a hybrid deep learning-anomaly detection strategy, was used to discover abnormalities in agricultural landscapes. This approach capitalizes on the intrinsic uniformity of these landscapes, delivering amazing accuracy in anomaly identification. The authors [8] further employed support vector machines for item identification and landscape categorization, attaining a significant classification accuracy of 91.6%. The practical usefulness of this approach was proven on a local, operating farm [11]. A thorough assessment of 135 significant articles from 2017 to 2022 offered insight into IoT applications within smart agriculture [12]. That detailed review underlines the crucial role of communication technologies in integrating IoT into agricultural operations, examining both the benefits and problems connected to IoT installations. The paper also suggests future research initiatives targeted at boosting agricultural sustainability, quality, and production through IoT improvements. In addition, a full evaluation of the security procedures within smart agriculture has been undertaken, concentrating on the authentication and access control mechanisms deployed by IoT applications [15]. That research presents a security architecture optimized for agri-tech and creates a threat model that defines probable hostile behaviors in agricultural environments. Another study investigates the role of information and communication technologies (ICTs) in agriculture, analyzing new dangers, weaknesses, and developments in ICT solutions [16].