Solar-powered motion sensor for farm monitoring and surveillance: a solar-powered assisted process

<ns3:p>The farming industry faces continuous threats from pest control and farm security issues because rodents cause significant damage to crops and disrupt farm operations. Traditional pest control methods require continuous human interaction which proves both resource-intensive and inefficient. Modern agricultural practices benefit from sustainable solutions through the combination of renewable energy with smart technologies. This research presents an innovative solar-powered motion-sensor system that utilizes OpenCV and YOLOv8 computer vision frameworks to autonomously detect and classify rodent intruders on farmland real-time. The system demonstrates its ability to detect and prevent rodent intruders according to initial testing results. The OpenCV/YOLO system uses motion sensor signals to analyze movement patterns before distinguishing rodents in their various groups. The solar-powered system operates continuously which decreases human intervention needs and enhances farm surveillance capabilities. The model demonstrates its capability to defend crops from rodent damage and enhance farm resistance against land degradation threats, resulting to improved crop yield and management. KPIs have been evaluable to prove the system’s efficiency and reliable to be used in agricultural practices to manage animal pests. The current system encounters problems with detecting wild animals beyond rodents as well as tracking rodent activity beneath ground level. Future developments could include improved pest capture systems alongside enhanced surveillance features for detecting both unauthorized human intruders and large animals. Future research should also invest in real-time implementation in fields with real data. The automated monitoring technology needs to be integrated with reliable sources of energy to create sustainable agricultural operations that are efficient and resilient, hence enhancing food security.</ns3:p>