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Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis

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  • Sambandh Bhusan Dhal
  • Muthukumar Bagavathiannan
  • Ulisses Braga-Neto
  • Stavros Kalafatis

Abstract

With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of data-driven methods and approaches for aquaponic cultivation remains a challenge. The objective of this research is to investigate statistical methods to make inferences using small datasets for nutrient control in aquaponics to optimize yield. In this work, we employed the Density-Based Synthetic Minority Over-sampling TEchnique (DB-SMOTE) to address dataset imbalance, and ExtraTreesClassifer and Recursive Feature Elimination (RFE) to choose the relevant features. Synthetic data generation techniques such as the Monte-Carlo (MC) sampling techniques were used to generate enough data points and different feature engineering techniques were used on the predictors before evaluating the performance of kernel-based classifiers with the goal of controlling nutrients in the aquaponic solution for optimal growth.[27–35]

Suggested Citation

  • Sambandh Bhusan Dhal & Muthukumar Bagavathiannan & Ulisses Braga-Neto & Stavros Kalafatis, 2022. "Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-15, August.
  • Handle: RePEc:plo:pone00:0269401
    DOI: 10.1371/journal.pone.0269401
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    References listed on IDEAS

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    1. Abubakar Abid & Martin J. Zhang & Vivek K. Bagaria & James Zou, 2018. "Exploring patterns enriched in a dataset with contrastive principal component analysis," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
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