IoT Controlled Aquaponic System

Prabha, Rajanie; Saranish, R Sri; Sowndharya, S.; Santhosh, AC; Varsha, R; Sumathi, K.

doi:10.1109/icaccs48705.2020.9074401

Cited by 12 publications

(2 citation statements)

References 3 publications

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“…Recently, Prabha et al [50] developed a Zigbee-based aquaponics monitoring system. An aquaponics system is a hydroponic system that gets its nutrient from fish waste in a nearby aquarium [51].…”

Section: Aeroponicsmentioning

confidence: 99%

Automated Hydroponic System using Wireless Sensor Networks

Musa¹,

Mabu²,

Modu³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Researchers have associated agriculture and food processing with adverse environmental impacts like; falls in the underground freshwater table, energy consumption, and high carbon emission. These factors have the worst effect on developing countries. Therefore, there is a need for on-demand food production techniques that require minimum resource utilization. For these reasons, scientists are now focusing their attention on hydroponics. Hydroponics is the process of growing crops without the use of soil. However, different components of the system need to be closely monitored and controlled. In this paper, we compared the performance of an automated hydroponic system using cluster-based wireless sensor networks against a multihop-based one. We used Simponics for the simulation. It is a simulator based on the OMNET++ framework. Simulation results show that both latency and energy overhead of the multihop network increases with the number of nodes. However, they stay constant on a cluster-based network.

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Section: Aeroponicsmentioning

confidence: 99%

Automated Hydroponic System using Wireless Sensor Networks

Musa¹,

Mabu²,

Modu³

et al. 2022

Adv. sci. technol. eng. syst. j.

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“…Internet of things (IOT) technology is universally applied in several industries. Prabha et al designed an aquaponics system and monitored the growth of fish as well as vegetables [8]. Purwandoko traced each parameter in the production process and established related knowledge and information with a rice traceability system [9].…”

Section: Introductionmentioning

confidence: 99%

Application of Regression Analysis to Achieve a Smart Monitoring System for Aquaculture

Hsu¹,

Chao²,

Wang

et al. 2020

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The consumption awareness of people in recent years has increased, with food safety becoming more and more important. While non-toxic products can be achieved by avoiding using too much antibiotics to control growth factors in a water environment, the measurement tools for dissolved oxygen on the market are very expensive and a great economic burden to fishermen. Thus, the purpose of this study is to design more economical measurement modules and algorithms for monitoring ponds. The research collected pond data through Oxidation-Reduction Potential (ORP), pH and temperature sensors, used regression analysis to infer Dissolved Oxygen (DO) by ORP and pH, and employed a real-time pond monitoring data map to figure out pond conditions. Compared with traditional equipment, findings show our approach reduces costs by about 20%, and increases production capacity and output value.

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