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Crop damage from animal intrusions poses economic challenges for small-scale farmers. Traditional surveillance systems are costly and internet-dependent, making them impractical for rural areas. This paper proposes a low-cost, AI-driven surveillance system using ESP32 CAM, Raspberry Pi, and YOLOv11x for real-time detection. The system processes video locally, reducing reliance on cloud infrastructure, and sends instant WhatsApp alerts via PyWhatKit. Field tests show 93% accuracy and a 7 s alert response time, making it a scalable and efficient solution. Future work includes infrared night vision and AI-based adaptive learning to enhance performance.

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Low-Cost Surveillance Systems for Crops Using IoT

  • 会议论文

  • 发表日期：

    2026年

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  • K. Naresh,
  • Kunj Goel,
  • Tilak Sharma

摘要

Crop damage from animal intrusions poses economic challenges for small-scale farmers. Traditional surveillance systems are costly and internet-dependent, making them impractical for rural areas. This paper proposes a low-cost, AI-driven surveillance system using ESP32 CAM, Raspberry Pi, and YOLOv11x for real-time detection. The system processes video locally, reducing reliance on cloud infrastructure, and sends instant WhatsApp alerts via PyWhatKit. Field tests show 93% accuracy and a 7 s alert response time, making it a scalable and efficient solution. Future work includes infrared night vision and AI-based adaptive learning to enhance performance.