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

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.

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Publication Details

Journal
Future Internet
Published
2025-02-06
DOI
https://doi.org/10.3390/fi17020070
Citations
8
Primary Topic
Food Supply Chain Traceability
Type
article
Field-Weighted Citation Impact
9.30
Controls
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article

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

Sayak Mukhopadhyay, A. S. M. Sanwar Hosen, Tahesin Samira Delwar, Mangal Singh et al.
8 citations
Future Internet
Food Supply Chain Traceability
9.30
article

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

Sayak Mukhopadhyay, A. S. M. Sanwar Hosen, Tahesin Samira Delwar, Mangal Singh, Yangwon Lee, Jee-Youl Ryu, Akshay Kumar
article en
8 citations

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.

Future InternetVol. 17(2)
Symbiosis International University (IN), Woosong University (KR), Pukyong National University (KR)
Openalex Percentile: Top 2%
Food Supply Chain Traceability
9.30
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