Narrow-Band IoT Applications in Precision Agriculture for Real-Time Environmental Monitoring and Crop Management

Precision agriculture is transforming traditional farming practices through data-driven approaches that enable more efficient management of resources such as water, nutrients, and pesticides. This study investigates the implementation of a Narrow-Band Internet of Things (NB-IoT) enabled precision agriculture system integrated with a Long Short-Term Memory (LSTM) deep learning model to optimize crop management through real-time environmental monitoring and data-driven decision-making. The system deployed sensors to monitor essential parameters, including soil moisture, temperature, humidity, light intensity, pH, and nutrient levels, enabling continuous data collection. The LSTM model was trained on historical data to predict crop health, irrigation needs, and pest risk, achieving a prediction accuracy of 92% for soil moisture and 89% for pest risk. Results showed that datadriven irrigation adjustments reduced water consumption by 10-13% while increasing crop yield by 10-13%. Real-time nutrient monitoring facilitated targeted fertilization, reducing over-application by 15% and improving resource efficiency. The study demonstrates that an NB-IoT and deep learning approach can significantly enhance crop productivity and sustainability in agriculture. This model offers a scalable and adaptive solution for precision farming, providing actionable insights for optimized resource use and environmental conservation.