Intelligent IoT and Deep Learning-Based Soil Monitoring System for Optimizing Tea Cultivation

Tea farming needs specific soil monitoring which enables both increased yields and better product quality. The assessment techniques for soil created limitations because they need significant human resources but lack current information therefore causing suboptimal resource management. This paper develops a Deep Learning and IoT-based Intelligent Soil Monitoring System dedicated to tea plantation assessment. The system uses IoT-enabled soil sensors to continuously record key parameters of moisture, temperature, pH, nitrogen (N), phosphorus (P) and potassium (K) as well as moisture levels. High-resolution analysis of soil health depends on HDLA that operates from data gathered through the cloud-based platform using CNN and Bi-LSTM capabilities. Implementing the HDLA model leads to improved extraction of features and temporal pattern recognition capabilities therefore enhancing the forecast of soil conditions. The CNN acts as an efficient spatial feature extractor for sensor data and the Bi-LSTM provides exact anomaly detection with predictive capabilities through its temporally oriented process. The system applies a Soil Health Index (SHI) to measure soil fertility values while determining suitability for tea cultivation. Researchers implemented testing of the system by utilizing actual monitoring data from various tea plantation sites. Research findings indicate that the proposed HDLA model successfully classified soil health with a rate of 96.2% which exceeded the performances of typical CNN at 91.2% and LSTM at 93.5%. The predictive capacity for detecting soil anomalies achieved an accuracy rate of 94.5% which shows the solid performance of the analytical method. Timely analysis using the proposed approach needed 27% less time than traditional cloud-based systems thus enabling faster agricultural decisions by farmers. Through its intelligent soil monitoring system, the system greatly enhances the speed of real-time soil evaluation to enable precise agricultural practices in tea agriculture.