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Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method.

Janez Trontelj Ml1, Olga Chambers1

  • 1Faculty of Electrical Engineering, Trzaska cesta 25, 1000 Ljubljana, Slovenia.

Sensors (Basel, Switzerland)
|July 2, 2021
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Summary

Machine learning enhances soil property prediction accuracy using optical spectroscopy. A multi-component nutrient strategy and principal component analysis improved results, outperforming simpler methods.

Keywords:
machine learningnutrients predictionprecision farmingsoil analysissoil categorysoil spectra

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Area of Science:

  • Agricultural Science
  • Data Science
  • Spectroscopy

Background:

  • Accurate soil property prediction is crucial for precision agriculture and environmental management.
  • Optical spectroscopy offers a rapid, non-destructive method for soil analysis.
  • Machine learning (ML) algorithms show potential for improving prediction accuracy.

Purpose of the Study:

  • To evaluate the effectiveness of various machine learning approaches for predicting soil properties using optical spectroscopy.
  • To compare the performance of different ML algorithms and nutrient characterization strategies.
  • To identify optimal data processing techniques, such as principal component analysis (PCA), for enhanced prediction.

Main Methods:

  • Comparison of six machine learning algorithms: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine (SVM), Least-Square SVM (LS-SVM), and Artificial Neural Network (ANN).
  • Investigation of multi-component versus single-component nutrient characterization strategies and varying category levels (3, 5, 13).
  • Analysis of soil samples from a local farm and diverse Slovenian locations, with and without Principal Component Analysis (PCA) using 5, 10, 20, and 50 components.

Main Results:

  • No single ML algorithm consistently provided the best prediction accuracy; simpler methods sometimes outperformed complex ones.
  • Multi-component nutrient characterization yielded significantly better prediction accuracy than single-component approaches.
  • Using 50 principal components in PCA demonstrated improved performance for machine learning models.
  • Soil from a local farm with similar texture showed better prediction accuracy compared to diverse Slovenian soils.

Conclusions:

  • Machine learning, particularly with optimized data preprocessing like PCA, significantly improves soil property prediction accuracy via optical spectroscopy.
  • A multi-component nutrient characterization strategy is recommended for more precise soil analysis.
  • The choice of ML algorithm and data dimensionality reduction techniques are critical factors for successful soil property prediction.