IoT-based automated surface-drip irrigation monitoring system prototype: a case of World Vegetable Center in Arusha, Tanzania
Loading...
Date
2024-08
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
NM-AIST
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.