In the heart of Indonesia, researchers have developed a smart irrigation system that could revolutionize how farmers manage water usage, particularly for mustard greens. The system, designed by Abdi Mulia Pranidana and his team at Universitas Malikussaleh, leverages fuzzy logic and IoT technology to optimize irrigation, promising significant water savings and improved crop yields.
The smart valve irrigation system uses an ESP32 microcontroller, DHT22 temperature sensor, and a capacitive soil moisture sensor to gather real-time environmental data. This data is then processed by a fuzzy logic engine, which determines the optimal irrigation intensity through centroid-based defuzzification. A web-based dashboard, developed using PHP and JavaScript, allows farmers to monitor temperature, soil moisture, and irrigation status in real time.
“Our system adapts to the changing environmental conditions, ensuring that the plants receive the right amount of water at the right time,” Pranidana explained. This adaptive approach resulted in a 35% reduction in water usage compared to traditional manual watering methods during a 12-hour test on mustard greens (Brassica juncea L.), while maintaining optimal soil moisture levels.
The implications for the agriculture sector are substantial. With water scarcity becoming an increasingly pressing issue, technologies that enhance water efficiency are invaluable. Pranidana’s system offers a scalable solution that can be easily integrated into existing farming practices. “This technology has the potential to transform smart agriculture by making it more sustainable and efficient,” Pranidana added.
The research, published in the Journal of Applied Informatics and Computing, highlights the potential of integrating fuzzy logic and IoT in agriculture. As the world grapples with climate change and resource depletion, such innovations are crucial for ensuring food security and sustainability.
The commercial impact of this research could be profound. Farmers can reduce water costs and improve crop yields, while agribusinesses can adopt this technology to enhance their sustainability credentials. The system’s adaptability means it can be tailored to various crops and climates, making it a versatile tool for modern agriculture.
Looking ahead, this research could pave the way for further advancements in smart agriculture. The integration of artificial intelligence and machine learning with IoT devices could lead to even more sophisticated systems capable of predicting and responding to environmental changes with greater precision. As Pranidana and his team continue to refine their technology, the future of smart agriculture looks increasingly promising.