Journal of Scientific Research and Reports

This study aimed to design, develop, and evaluate a dual-operated AI-based Wi-Fi weeder for precision weed management in small and medium-scale farms. The weeder was equipped with a Raspberry Pi microcontroller and a camera module to detect crops and weeds in real-time, enabling autonomous operation. Laboratory tests evaluated Wi-Fi connectivity, which was effective up to 50 m, and battery/motor performance, while field trials assessed weeding efficiency, field efficiency, useful work coefficient, time/energy ratio, and economic performance. The weeder achieved a weeding efficiency of 98.07% and a field efficiency of 59.68%, with a useful work coefficient of 84.5% and a time/energy ratio of 72.1%, indicating high productivity and efficient energy use. Economic analysis showed an average profit gain of ₹68.5 per hour, demonstrating cost-effectiveness for small and medium-scale farmers. The results indicate that the AI-based Wi-Fi weeder is an effective, energy-efficient, and economically viable solution for automated weed control, reducing labor dependency and minimizing crop damage. Its performance highlights the potential for precision agriculture applications, and future improvements in navigation and AI detection are expected to further enhance efficiency and adaptability.