An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups

Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time smart sensing and actuation system for controlling...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-01, Vol.23 (1), p.451
Main Authors: Dhal, Sambandh Bhusan, Mahanta, Shikhadri, Gumero, Jonathan, O'Sullivan, Nick, Soetan, Morayo, Louis, Julia, Gadepally, Krishna Chaitanya, Mahanta, Snehadri, Lusher, John, Kalafatis, Stavros
Format: Article
Language:English
Subjects:
Artificial intelligence
Calcium
Closed loops
Control
Correlation analysis
ExtraTreesClassifier
Feature selection
Feedback control
Heavy metals
hydroponic
Hydroponics
Identification and classification
Iron
Lactuca
Lettuce
Linear Support Vector Machine
Machine Learning
Measurement
Metals, Heavy
Monitoring systems
pairwise correlation
Reagents
Real time
Seeds
Support Vector Machine
Support vector machines
Technology application
Xgboost
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Summary:Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time smart sensing and actuation system for controlling heavy metal concentrations in aquaponic solutions. Our solution entails sensing the nutrient concentrations in the hydroponic solution, specifically calcium, sulfate, and phosphate, and sending them to a Machine Learning (ML) model hosted on an Android application. The ML algorithm used in this case was a Linear Support Vector Machine (Linear-SVM) trained on top three nutrient predictors chosen after applying a pipeline of Feature Selection methods namely a pairwise correlation matrix, ExtraTreesClassifier and Xgboost classifier on a dataset recorded from three aquaponic farms from South-East Texas. The ML algorithm was then hosted on a cloud platform which would then output the maximum tolerable levels of iron, copper and zinc in real time using the concentration of phosphorus, calcium and sulfur as inputs and would be controlled using an array of dispensing and detecting equipments in a closed loop system.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23010451