IoT-based automated surface-drip irrigation monitoring system prototype: a case of World Vegetable Center in Arusha, Tanzania

Abstract

Irrigation is pivotal for supplementing water, amidst climate change globally and surface-drip irrigation provides an efficient plant-watering method with minimal mineral depletion. In Tanzania, Africa, manual surface-drip irrigation persists despite attempts at SMS-based automation, leading to time-consuming routine irrigation tasks. Existing studies focus on XBee modules in their transparent operation mode, limiting scalability due to lack of information such as source addresses as well as lack of support for multiple sensor nodes. This project, carried out in Arusha, Tanzania, employed a wireless sensor network using XBee modules configured in API mode for soil-data collection and transmission, utilizing Node-RED for remote actuator control and soil data visualization. By employing JavaScript functions, data bytes were extracted, including transmitter addresses, from each sensor node, enhancing scalability. Sensors measured soil moisture, temperature, humidity, air temperature, and ultraviolet radiation to determine irrigation requirements. The outcome, an IoT-based Automated Surface-drip Irrigation Monitoring System Prototype was tested against user and system requirements. Remarkably, the system reduced irrigation control time by 96%, compared to manual control, by automatically triggering on the pump and valves when minimum soil moisture threshold is reached and turning them off once the maximum moisture threshold is reached. It also supported over-the-air firmware updates, to reduce maintenance costs, enabling the integration of technologies like digital twins. The system can integrate with existing manual irrigation systems, offering a cost-effective automation solution. Future improvements could involve incorporating machine learning for crop water requirement forecasting, expanding the sensor node network and extensive testing under different farm environments.