Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays

Area of Science:

• Agricultural Science
• Sensor Technology
• Environmental Science

Background:

• Global population growth necessitates increased food production.
• Precision agriculture offers strategies to enhance crop yield sustainably.
• Site-specific fertilization requires accurate soil nutrient monitoring.

Purpose of the Study:

• To develop and compare impedimetric multisensor arrays for soil nutrient analysis (N, P, K).
• To evaluate the effectiveness of ion-selective membranes versus layer-by-layer films in sensor arrays.
• To apply multidimensional projection and machine learning for sensor calibration and data analysis.

Main Methods:

• Development of two impedimetric multisensor arrays: one with ion-selective membranes and another with layer-by-layer films.
• Analysis of soil samples enriched with macronutrients (N, P, K).
• Application of multidimensional projection techniques and machine learning (decision tree algorithm) for calibration.

Main Results:

• Both sensor arrays effectively distinguished soil samples.
• The ion-selective membrane array demonstrated higher sensitivity to potassium (K) content.
• Machine learning algorithms optimized sensor calibration by selecting best frequencies and sensors.

Conclusions:

• Impedimetric multisensor arrays are viable tools for precise soil nutrient analysis.
• Ion-selective membranes offer enhanced sensitivity for specific nutrient detection, particularly potassium.
• These advancements support environmentally friendly agriculture through optimized fertilizer application and improved crop yields.