Open Access Open Access  Restricted Access Subscription Access

Machine Learning Based Smart Aquaponics Farming System

Ragini Sharma, Sakshi Sampat Dhapte, Srushti Shyam Pandirkar, Muskan Murad Jamate


For many years, researchers have been studying nutrient management in aquaponic systems. Most have concentrated on adequate nutrition control in an aquaponic setup, but there has been relatively little study on commercial scale applications. For plant growth, it is necessary to measure the level of nutrients present in the soil mixture. In our model, the input data was sourced on some interval of time basis from three commercial aquaponic farms. To rank the features in order of relevance, and feature selection, approaches such as the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were utilised. Based on the plants and fish, ammonium and nitrates were discovered to be the top two nutrient predictors. The historical dataset's median nutrient levels served as the appropriate concentrations to be maintained in the aquaponic solution in which Ashwagandha was growing. Vernier sensors were utilised to measure nutrient values, and actuator systems were created to discharge the necessary nutrient into the ecosystem through a closed loop. A digital system is created which gives information about the fertiliser(s) required for their crops and the data sensed by the sensors is stored in the cloud and analysed, based on which, suggestions for the growth of the suitable crop are made.


Machine learning, random forest, sensors, aquaponics, IoT, AutoML

Full Text:



Pillay TVR, Kutty MN. Aquaculture: principles and practices. 2nd Edn. Blackwell publishing; 2005.

Jensen MH. Hydroponics. HortScience. 1997; 32(6): 1018–1021.

Pillay TVR. Aquaculture and the Environment. John Wiley & Sons; 2008.

Jones Jr JB. Hydroponics: a practical guide for the soilless grower. CRC press; 2016.

Roberto K. How-to hydroponics. New York: Future Garden, Inc.; 2003.

Reyes Yanes A, Martinez P, Ahmad R. Towards automated aquaponics: A review on monitoring, IoT, and smart systems. J Clean Prod. 2020; 263: 121571.

Mahanta S, Habib MR, Moore JM. Effect of High-Voltage Atmospheric Cold Plasma Treatment on Germination and Heavy Metal Uptake by Soybeans (Glycine max). Int J Mol Sci. 2022; 23(3): 1611.

Dutta Abhay, et al. IoT based aquaponics monitoring system. 1st KEC Conference Proceedings. 2018; 1: 75–80.


  • There are currently no refbacks.

Copyright (c) 2022 Research & Reviews: A Journal of Embedded System & Applications