RICEGUARD: IoT Noise-Enabled Scarecrow with Image Processing through Machine Learning and SMS Notification for Rice Crop Protection Enhancement - Auxtero, Angelo; Bendijo, Welcon; Capio, Jubiel G; Jacamos, April Rose; Sudaria, Phoebe Ruth Alithea Bacotot; Orozco, Shiela Mae M.; Gumonan, Kenn Migan Vincent (International Journal of Computing Sciences Research, 2025)
Purpose – This study intends to build a smart scarecrow that protects the rice yield especially when the grain is almost ripe. This will offer a dependable and effective method for safeguarding rice crops through the Internet of Things and machine learning image processing. Method – The agile methodology was utilized in this study since it is suitable for the device's iterative development process. Results – The mean result of the system evaluation is rated at 4.45, with an excellent interpretation from the user using the ISO ISO/IEC 25010:2011 tool. Conclusion – The researchers were able to develop smart water irrigation for rice farming using IoT and micro-controller devices with solar panel support and the respondents also agreed that the Smart water irrigation for rice farming using IoT and micro-controller devices with solar panel support is practical and valuable. Recommendations – Future researchers that will focus on the relevant study may consider integrating an autofocus camera into the system to enhance the image resolution, making it significantly more effective in threat detection within the covered area. It will also contribute to stability and enhance tracking capabilities, especially for moving subjects. Research Implications - The research can help rice farmers protect their crops from bird attacks, especially when the crop is almost ready for harvest. This will enhance the farmers' production, leading to a higher income to support their families.