Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection

Tahesin Samira Delwar, Sayak Mukhopadhyay, Akshay Kumar, Mangal Singh, Yang-won Lee, Jee-Youl Ryu () and A. S. M. Sanwar Hosen ()
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Tahesin Samira Delwar: Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea
Sayak Mukhopadhyay: Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, India
Akshay Kumar: Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, India
Mangal Singh: Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, India
Yang-won Lee: Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea
Jee-Youl Ryu: Department of Information and Communication Engineering, Pukyong National University, Busan 48513, Republic of Korea
A. S. M. Sanwar Hosen: Department of Artificial Intelligence and Big Data, Woosong University, Daejeon 34606, Republic of Korea

Future Internet, 2025, vol. 17, issue 2, 1-43

Abstract: This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of Things (IoT), successfully safeguarding crops and reducing agricultural losses. This study involves a thorough examination of five models—Inception, Xception, VGG16, AlexNet, and YoloV8—against three different datasets. The YoloV8 model emerged as the most promising, with exceptional accuracy and precision, exceeding 99% in both categories. Following that, the YoloV8 model’s performance was compared to previous study findings, confirming its excellent capabilities in terms of intrusion detection in agricultural settings. Using the capabilities of the YoloV8 model, an IoT device was designed to provide real-time intrusion alarms on farms. The ESP32cam module was used to build this gadget, which smoothly integrated this cutting-edge model to enable efficient farm security measures. The incorporation of this technology has the potential to transform farm monitoring by providing farmers with timely, actionable knowledge to prevent possible threats and protect agricultural production.

Keywords: agricultural security; animal detection; computer vision; crop protection; early warning system; IoT; intrusion detection; image processing; machine learning; sensor technology; smart farming; wildlife intrusion; Yolo V7 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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