Evaluation of the Performance of an Ultrasonic-IoT Based Rice Field Rat Repellent System in Reducing Attack Intensity and Crop Losses
DOI:
https://doi.org/10.35335/computational.v14i3.287Keywords:
Ultrasonic rodent repellent, Internet of Things (IoT), Rice field pest control, Rodent attack intensity, Smart and sustainable agricultureAbstract
Rodent infestation remains a major constraint in rice production, causing significant yield losses and threatening agricultural sustainability. Conventional rodent control methods, such as chemical rodenticides and manual trapping, often exhibit limited effectiveness and pose environmental and health risks. This study aims to evaluate the performance of an ultrasonic–Internet of Things (IoT)-based rice field rodent repellent system in reducing attack intensity and crop yield losses under real field conditions. The research employed a comparative field experiment conducted over one planting season, involving treated plots equipped with the ultrasonic–IoT system and untreated control plots managed using conventional practices. Rodent attack intensity was assessed through indicators including the percentage of damaged rice clumps, active burrow counts, and observable rodent activity, while yield loss was measured based on harvested grain output (kg/ha). System performance was further evaluated through the consistency of ultrasonic signal emission and the reliability of IoT-based data transmission. The results demonstrate a clear reduction in rodent attack intensity in treated fields compared to control fields, accompanied by a significant decrease in yield loss. The ultrasonic–IoT system operated reliably throughout the observation period, maintaining stable signal emission and continuous data logging despite variable field conditions. However, environmental factors such as weather variability and rodent migration patterns influenced system effectiveness to some extent. Overall, the findings indicate that the ultrasonic–IoT-based rodent repellent system is an effective, environmentally friendly, and data-driven approach that supports smart and sustainable agriculture. The system is best implemented as part of an integrated pest management strategy to enhance long-term effectiveness and scalability.
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