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Article

A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations

1
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 79016, USA
2
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 79016, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Raffaele Bruno
Sensors 2022, 22(9), 3510; https://doi.org/10.3390/s22093510
Received: 9 March 2022 / Revised: 1 May 2022 / Accepted: 3 May 2022 / Published: 5 May 2022
(This article belongs to the Section Internet of Things)
Nutrient regulation in aquaponic environments has been a topic of research for many years. Most studies have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been conducted on commercial-scale applications. In our model, the input data were sourced on a weekly basis from three commercial aquaponic farms in Southeast Texas over the course of a year. Due to the limited number of data points, dimensionality reduction techniques such as pairwise correlation matrix were used to remove the highly correlated predictors. Feature selection techniques such as the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors, and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentration to be maintained in the aquaponic solution to sustain healthy growth of tilapia fish and lettuce plants in a coupled set-up. To accomplish this, Vernier sensors were used to measure the nutrient values and actuator systems were built to dispense the appropriate nutrient into the ecosystem via a closed loop. View Full-Text
Keywords: aquaponic; pairwise correlation matrix; XGBoost; Recursive Feature Elimination; ExtraTreesClassifier; median; closed loop aquaponic; pairwise correlation matrix; XGBoost; Recursive Feature Elimination; ExtraTreesClassifier; median; closed loop
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MDPI and ACS Style

Dhal, S.B.; Jungbluth, K.; Lin, R.; Sabahi, S.P.; Bagavathiannan, M.; Braga-Neto, U.; Kalafatis, S. A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations. Sensors 2022, 22, 3510. https://doi.org/10.3390/s22093510

AMA Style

Dhal SB, Jungbluth K, Lin R, Sabahi SP, Bagavathiannan M, Braga-Neto U, Kalafatis S. A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations. Sensors. 2022; 22(9):3510. https://doi.org/10.3390/s22093510

Chicago/Turabian Style

Dhal, Sambandh B., Kyle Jungbluth, Raymond Lin, Seyed P. Sabahi, Muthukumar Bagavathiannan, Ulisses Braga-Neto, and Stavros Kalafatis. 2022. "A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations" Sensors 22, no. 9: 3510. https://doi.org/10.3390/s22093510

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