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Hybrid ensemble-based machine learning model for predicting phosphorus concentrations in hydroponic solution.

Rozita Sulaiman1, Nur Hidayah Azeman2, Mohd Hadri Hafiz Mokhtar1

  • 1Photonics Technology Laboratory, Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|September 14, 2023
PubMed
Summary

Hybrid machine learning models accurately predict phosphorus concentration in hydroponic systems using absorbance data. These advanced models offer improved accuracy over single models for essential nutrient management.

Keywords:
Ensemble techniqueHydroponicMachine learningNutrientSpectroscopy

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Area of Science:

  • Agricultural Science
  • Data Science
  • Environmental Science

Background:

  • Accurate phosphorus measurement is crucial for hydroponics to optimize plant growth and prevent environmental issues.
  • Existing methods for phosphorus detection can be time-consuming or require labels, limiting their application.

Purpose of the Study:

  • To evaluate the effectiveness of hybrid machine learning models for label-free phosphorus concentration prediction.
  • To compare the performance of hybrid models against single machine learning models using absorbance data.

Main Methods:

  • Employed three base classifiers: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).
  • Utilized ensemble techniques (voting, bagging, stacking) to create hybrid models.
  • Analyzed accuracy and computational time for both single and hybrid models.

Main Results:

  • Support Vector Machine (SVM) achieved the highest accuracy (99.6%) among single models.
  • The stacking hybrid model combining SVM, KNN, and RF yielded the highest accuracy (99.73%) with efficient computational time (36.18 s).
  • Hybrid models demonstrated superior accuracy in predicting phosphorus levels compared to individual models.

Conclusions:

  • Machine learning effectively distinguishes phosphorus concentrations in hydroponic systems.
  • Hybrid machine learning techniques enhance prediction accuracy for phosphorus levels without requiring labels.
  • The developed hybrid models offer a promising solution for rapid, label-free phosphorus monitoring in hydroponics.