Tobacco Seedling Automation Management
Keywords:
Keywords: Internet of Things, Automatic Irrigation, Tobacco Nursery, Smart Agriculture, Resource EfficiencyAbstract
Tobacco seedling cultivation is an early stage that determines the success of tobacco cultivation because it directly affects seed quality and plant productivity in the next phase. Seedling cultivation practices at the farmer level are generally still carried out conventionally, with manual watering and unmeasured environmental monitoring, which can potentially cause water wastage, high workload, and unsuitable planting media conditions. The development of Internet of Things (IoT) technology offers a data-driven approach to support automation and efficiency in agricultural management. This study aims to implement and evaluate an IoT-based tobacco seedling automation management system to improve water use efficiency and reduce farmers' working hours. The study uses a design and field test method with a quantitative and qualitative approach. The system was developed by integrating soil moisture sensors, air temperature and humidity sensors, rain sensors, a time scheduling module, and instant messaging-based notifications. The evaluation was conducted through system performance testing, observation of watering responses, and comparison of water use and farmers' working hours before and after the system implementation. The results showed that the system was able to work stably and responsively. Irrigation can be carried out adaptively based on soil moisture conditions and stopped automatically when rain is detected. The application of the system has successfully reduced water usage significantly and reduced farmers' working time from 420–595 minutes per week to 210–315 minutes per week, or a reduction of around 45%. In conclusion, the IoT-based tobacco seedling automation management system is effective in improving seedling management efficiency and supporting smart farming practices. Further research is recommended to develop the system on a larger scale and utilize historical data for more adaptive decision-making.
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