International Research Journal of Education and Technology

This abstract introduces a soil monitoring system designed to enhance agricultural practices through real-time monitoring of soil moisture and nutrition levels. The system employs industry-standard sensors, including a soil moisture sensor and an NPK sensor, along with an ESP8266 microcontroller equipped with a MAX485 TTL to RS-485 interface module. These sensors are embedded in the soil, providing continuous data on soil conditions. The soil moisture sensor assesses soil water content, while the NPK sensor measures vital nutrients like nitrogen, phosphorus, and potassium. To enable wireless data transmission, the ESP8266 microcontroller acts as a central node, interfacing with the sensors via the RS-485 protocol, known for its reliability in long-distance and multi-node communication. Moreover, the system incorporates machine learning to analyze collected data, constructing a model that identifies patterns and correlations between soil parameters, crop types, and required fertilization based on historical data. This model generates predictions, recommendations, and early alerts for optimal irrigation schedules and fertilizer applications. The processed data, machine learning insights, and recommendations are presented through a user-friendly monitoring dashboard accessible via web or mobile interfaces. This dashboard offers real-time visualizations, alerts, and analytics, enabling farmers to make informed decisions remotely. Overall, this soil monitoring system offers benefits such as real-time data collection, wireless connectivity, and a user-friendly dashboard. By merging sensor technology with advanced communication modules and machine learning, it empowers farmers with actionable insights, leading to improved crop yield, reduced water usage, and optimized fertilizer utilization.

Keywords: Soil moisture, Nutrient levels, Real-time sensors, Fertilizer recommendations, Crop guidance, Interactive dashboard.