CALIBRATION METHOD OF COMMERCIAL NPK SOIL SENSOR BASED ON ARTIFICIAL NEURAL NETWORK

Abstract

This invention relates to a method for calibrating a commercial soil sensor based on artificial neural networks to improve the accuracy of soil nutrient estimation. This method includes the steps of obtaining uncalibrated soil sensor output data including electrical conductivity (EC), Nitrogen (N), Phosphorus (P), Potassium (K), and environmental parameters such as pH, humidity, and soil temperature; obtaining reference data from laboratory tests on the same soil sample; training an artificial neural network model using the sensor data and reference data to model the non-linear relationship between sensor parameters and environmental parameters against the reference values; and processing the sensor data using the trained model to produce calibrated soil nutrient values ​​of N, P, K, and pH. Thus, this invention allows for improved accuracy of commercial soil sensor data without requiring laboratory testing at each measurement point.