Aquaponic Growbed Water Level Control Using Fog Architecture

Romli, M. A.; Daud, Shuhaizar; Raof, Rafikha Aliana A.; Ahmad, Z. A.; Mahrom, N.

doi:10.1088/1742-6596/1018/1/012014

Cited by 7 publications

(2 citation statements)

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“…Through the fish activity sensing, the fish metabolic rate was calculated using the regression analysis [64]. A real-time water quality monitoring system is developed based on assessing time series motion trajectories of live fish and using a neural network algorithm to estimate the frequency of pattern changes in these trajectories [65]. The author developed an ML-based IoT system for optimizing nutrient supply in the aquaponics system.…”

Section: Machine Learning Techniques Used In Aquaponicsmentioning

confidence: 99%

Imperative Role of Automation and Wireless Technologies in Aquaponics Farming

Gayam

Jain

Gehlot

et al. 2022

Wireless Communications and Mobile Computing

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Food and agriculture are significant aspects that can meet the food demand estimated by the Food Agriculture Organization (FAO) by 2050. In addition to this, the United Nations sustainable development goals recommended implementing sustainable practices to meet food demand to achieve sustainability. Currently, aquaponics is one of the sustainable practices that require less land and water and has a low environmental impact. Aquaponics is a closed-loop and soil-less method of farming, where it requires intensive monitoring, control, and management. The advancement of wireless sensors and communication protocols empowered to implementation of an Internet of Things- (IoT-) based system for real-time monitoring, control, and management in aquaponics. This study presents a review of the wireless technology implementation and progress in aquaponics. Based on the review, the study discusses the significant water and environmental parameters of aquaponics. Followed by this, the study presents the implementation of remote, IoT, and ML-based monitoring of aquaponics. Finally, the review presents the recommendations such as edge and fog-based vision nodes, machine learning models for prediction, LoRa-based sensor nodes, and gateway-based architecture that are beneficial for the enhancement of wireless aquaponics and also for real-time prediction in the future.

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