Intelligent Monitoring and Management of Soil Quality based on Deep Learning Algorithm

Traditional soil quality monitoring methods suffer from long cycles, high costs, and poor real-time capabilities, hindering precision agriculture. Deep learning offers promising solutions to these limitations. This study proposes an intelligent soil monitoring system integrating IoT sensors, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) models. CNN extracts spatial features from soil images, while LSTM analyzes time-series sensor data (e.g., pH, moisture, conductivity) for dynamic prediction.Four experiments demonstrated the system’s superiority: (1) LSTM achieved higher pH prediction accuracy (e.g., 6.41 vs. actual 6.42) than linear regression; (2) Real-time response averaged 0.16 seconds; (3) Strong adaptability across soil types (sandy, loam, clay); (4) Sustained stability in 30-day monitoring (max LSTM error: 0.23). The CNN-LSTM framework significantly enhances monitoring accuracy, speed, and reliability for precision soil management. Future work will focus on multi-source data fusion and large-scale field validation.