Development and Performance Evaluation of an IoT-Based Smart Irrigation System with Real-Time Data Monitoring for Beetroot (Beta vulgaris L.)
This study developed and evaluated a low-cost, ESP32-based IoT smart irrigation system for beetroot in Rwanda that successfully demonstrated functional real-time monitoring and a 45.1% reduction in water usage compared to manual methods, though further replicated trials are needed to confirm agronomic benefits.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Water is the lifeblood of agriculture, yet in many parts of the world, it is a scarce resource that must be used with extreme care. For centuries, farmers have relied on their own judgment or fixed schedules to decide when to water their crops, often guessing whether the soil is too dry or too wet. This guesswork can lead to wasted water or stressed plants. In recent years, a field known as precision agriculture has emerged, using small electronic sensors to measure the exact condition of the soil and the weather. These devices, part of a broader network called the Internet of Things, allow machines to make decisions about watering without human help. The goal is simple: to give plants just enough water to thrive while saving every drop possible for the future.
In the Eastern Province of Rwanda, where rainfall can be unpredictable, a team of researchers set out to test whether such a smart system could work for a specific, vital crop: the beetroot. Working at a demonstration farm in Rwamagana, they built a low-cost, automated irrigation system designed to monitor the soil and water the plants only when necessary. The device was constructed around a small computer chip capable of connecting to the internet, paired with sensors that could "feel" the moisture in the ground, sense the temperature and humidity of the air, and detect when it was raining. The system was programmed with clear rules: if the soil became too dry, the machine would turn on a water pump; if the soil became sufficiently wet, or if rain was falling, the machine would stop.
The researchers spent time designing the physical layout of the system, creating a three-dimensional model to ensure all the parts fit together securely before building the actual prototype. They assembled the electronics inside a waterproof box, connected a water pump to a storage tank, and buried a sensor near the roots of the beetroot plants. To make sure the sensor could accurately tell the difference between dry, damp, and soaking wet soil, they first tested it in controlled conditions. The sensor proved reliable, giving distinct readings for each state of the soil. Once the system was ready, they planted beetroot seeds and let the machine take over the watering duties for thirty days.
Over the course of the trial, the system cycled through ten irrigation events. Each time the soil moisture dropped to a specific low point, the machine detected the change and activated the pump. The water flowed for just over four minutes, delivering a measured amount of liquid to the roots before the soil reached a target wetness level and the pump shut off. The entire process, from the moment the soil dried out enough to trigger the sensor to the moment the pump actually started spinning, took less than three seconds on average. When it rained, the system's rain sensor acted as a safety switch, instantly blocking the pump from turning on even if the soil was dry, preventing any unnecessary watering.
The results showed a clear difference between the smart system and the traditional method used on nearby plots. While the automated system used about thirty-six liters of water per cycle, the manually irrigated plots nearby required sixty-five liters for the same task. This meant the smart system saved nearly half the water used by the human operators. The beetroot plants themselves grew well under this automated care, increasing in height by nearly twenty centimeters over the month. The researchers noted that the system successfully maintained the soil within a specific range of moisture, keeping the plants hydrated without drowning them.
However, the authors are careful to note that this was a single, short-term test. Because the study did not include multiple groups of plants for comparison or measure the final harvest yield, the results demonstrate that the machine works and saves water, but they do not yet prove that it produces more food than traditional methods. The study serves as a functional proof of concept, showing that inexpensive, locally available technology can be used to automate irrigation in Rwanda. The researchers suggest that the next steps involve testing the system over several seasons and on different types of crops to see if these water savings and healthy growth can be sustained over the long term. For now, the work stands as a practical demonstration that a simple, sensor-driven machine can take the guesswork out of watering, offering a potential path toward more efficient farming in water-scarce regions.
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