A robust IoT-enabled smart irrigation framework that leverages the ESP32 microcontroller and a suite of environmental sensors integrated with machine learning for dynamic decision-making integrated with machine learning for dynamic decision-making is presented.
The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture and can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.
Experimental results demonstrate reliable sensing, stable multi-node operation, efficient wireless communication, and effective automatic irrigation control, making the proposed model an affordable, scalable, and energy-efficient solution for smart agriculture.
Efficient irrigation management and nutrient utilization are essential factors in improving pak choy productivity. This study proposes an Internet of Things (IoT)-based automated irrigation system that integrates an ESP32 microcontroller, soil moisture sensors, rain sensors, Telegram Bot, and n8n workflow automation. T...
Agricultural production is dependent on a large amount of freshwater resources consumed worldwide, thus managing irrigation efficiently can create sustainable agriculture. Traditional irrigation methods generally rely on fixed schedules with no consideration given to the actual environmental and soil conditions present...
Most drip systems on small farms water the field on a fixed timer. They do not check how wet the soil already is or whether rain is coming, so a lot of water is wasted. This paper describes a low-cost system that fixes this. It reads soil moisture, temperature, humidity, pH, and NPK using sensors wired to a Raspberry P...
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