Abstract
This paper addresses the problem of predicting soil properties using a combination of optical spectroscopic and electrical impedance methods. A comparative analysis between the most common machine learning methods such as Random Forest, Naive Bayes, Support Vector Machine, Decision Tree and Artificial Neural Network was performed using our research dataset consisting of 50 soil samples. The results indicate that none of the methods showed the best performance for nutrients prediction when only optical or electrical impedance spectroscopy measurements were used. Then, the influence of the principal components was validated to improve the machine learning performance. Their negative influence on the overall accuracy was found. Finally, the influence of the nutrient category on the prediction was validated, where similar results were found for 3-level grade and 5-level grade systems indicating a possibility for more precise and accurate soil characterization. In addition, the work shows the importance of repeated measurements for each soil sample, which can improve the overall accuracy.
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