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 the 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 equipment in a closed loop system.
Dhal, Sambandh & Mahanta, Shikhadri & Gumero, Jonathan & O’Sullivan, Nick & Soetan, Morayo & Louis, Julia & Gadepally, Krishna & Mahanta, Snehadri & Lusher II, John & Kalafatis, Stavros. (2023). An IoT-Based Data-Driven Real-Time Monitoring System for Control of Heavy Metals to Ensure Optimal Lettuce Growth in Hydroponic Set-Ups. Sensors. 23. 451. 10.3390/s23010451.
Read the entire paper here researchgate.net