In the heart of Indonesia, a revolution in urban farming is brewing, and it’s all thanks to a cutting-edge system developed by Wizman Rofiansyah and his team at Telkom University in Bandung. Imagine a world where city dwellers can grow fresh produce in their apartments, offices, or even on rooftops, all while ensuring optimal plant health and minimal resource waste. This isn’t a distant dream but a reality made possible by the HydroFarm project, an innovative IoT-based control and monitoring system for hydroponic plant growth.
Hydroponics, the method of growing plants in nutrient-rich water rather than soil, has long been hailed as a solution for urban farming. However, managing the precise environmental conditions required for successful hydroponic growth has been a challenge. Enter HydroFarm, which integrates advanced image processing techniques and mobile applications to create a seamless, user-friendly system that addresses these very challenges.
At the core of HydroFarm is a network of sensors that monitor essential parameters such as temperature, humidity, pH, and nutrient levels. These sensors, connected to an ESP32 microcontroller, provide real-time data that is crucial for maintaining the delicate balance needed for plant growth. But what sets HydroFarm apart is its use of a convolutional neural network (CNN) for plant health assessment through image processing. This technology allows the system to analyze images of plant leaves, categorizing them as healthy or unhealthy with remarkable accuracy.
“The CNN can detect even the slightest changes in leaf appearance, which are often indicative of underlying issues,” explains Rofiansyah. “This early detection is crucial for preventing the spread of diseases and ensuring a healthy harvest.”
The mobile application, developed in Kotlin, is the user’s gateway to the HydroFarm system. It not only provides real-time data and alerts but also offers automated and manual control over nutrient delivery systems. This level of control is a game-changer for urban farmers, who often face space and resource constraints. “With HydroFarm, users can monitor and control their hydroponic systems from anywhere, at any time,” says Rofiansyah. “This flexibility is key to making urban farming accessible and sustainable.”
The potential commercial impacts of this technology are vast. For one, it could revolutionize the way we think about food security in urban areas. With HydroFarm, cities could become self-sustaining food producers, reducing the need for long-distance transportation and the associated carbon footprint. Moreover, the precision and efficiency of HydroFarm could lead to significant cost savings for urban farmers, making hydroponic farming a more viable and attractive option.
But the implications go beyond just urban farming. The integration of IoT and deep learning in HydroFarm could pave the way for similar systems in other sectors, such as agriculture and energy. For instance, the same principles could be applied to monitor and control solar farms, ensuring optimal energy production and reducing waste.
The HydroFarm system has already shown impressive results, with a 96% accuracy rate in detecting plant health conditions and a system usability scale (SUS) evaluation score of 81.875, categorizing the application as excellent and user-friendly. These findings, published in PeerJ Computer Science, underscore the potential for scaling this model to improve food security and promote sustainable agricultural practices in densely populated areas.
As we look to the future, it’s clear that technologies like HydroFarm will play a pivotal role in shaping our cities and our world. By harnessing the power of IoT and deep learning, we can create systems that are not only efficient and sustainable but also accessible and user-friendly. And who knows? The next time you enjoy a fresh salad, it might just have been grown in an apartment down the street, thanks to the innovative work of Wizman Rofiansyah and his team.