Virtual Sensor for Efficient Soil Fertilizer and Water Content Differentiation

Soil degradation issues, such as salinization, waterlogging, and erosion, pose significant challenges in agricultural irrigation and fertigation. Conventional laboratory methods for analyzing soil conditions are often cost-prohibitive, time-intensive, and require specialized personnel. This study investigates the application of a virtual sensor, incorporating RGB and LDR modules, to measure fertilizer concentration and water content in soil, as well as to differentiate between water and fertilizer. Statistical analysis of the acquired data using ANOVA revealed significant differences between samples under blue, yellow, and magenta lights, with p-values of 0.0425, 0.018, and 0.0425, respectively. Machine Learning (ML) techniques were employed to enhance the system's performance, yielding an 87.5% accuracy in binary classification using the K-Nearest Neighbors (KNN) and Euclidean models. Additionally, regression models demonstrated R2 values of 0.802 and 0.891 for irrigated and ferti-irrigated soils, respectively. These results suggest that the proposed optical sensor system is a promising, cost-effective alternative to traditional methods, though further validation in real-world applications is necessary.