Data Acquisition and Monitoring Dashboard for IoT Enabled Aquaponics Facility

被引:10
作者
Abbasi, Rabiya [1 ]
Martinez, Pablo [2 ]
Ahmad, Rafiq [1 ]
机构
[1] Univ Alberta, Dept Mech Engn, Aquapon Learning Factory AllFactory 4 0, Edmonton, AB, Canada
[2] Northumbria Univ, Mech & Construct Engn Dept, Newcastle Upon Tyne, Tyne & Wear, England
来源
2022 10TH INTERNATIONAL CONFERENCE ON CONTROL, MECHATRONICS AND AUTOMATION (ICCMA 2022) | 2022年
基金
加拿大自然科学与工程研究理事会;
关键词
smart aquaponics; cloud computing; big data; smart monitoring; quality assessment;
D O I
10.1109/ICCMA56665.2022.10011594
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aquaponics is an emerging field of agriculture that has great potential to ensure food security and sustainability. It presents a closed-loop ecosystem that synergizes fish farming (aquaculture) and soilless plant growing (hydroponics). As aquaponics is a simulation of a natural ecosystem, a significant number of factors are involved in its management. For instance, constant monitoring of water quality, environmental parameters, and illumination is required to achieve healthy growth of fish and plants. In combination with cloud computing technology, the internet of things (IoT) provides a platform to collect data, monitor systems, detect abnormal conditions, and rectify problems in the system without human intervention. This research project presents a framework that involves the development of a cloud-based dashboard for data acquisition and monitoring of an aquaponics facility. The data from a wireless sensing module (measuring pH, water temperature, electroconductivity, light intensity, air humidity, and air temperature) and several cameras installed in an aquaponics farm are uploaded wirelessly to the dashboard. These images are saved on the dashboard with their relevant sensor measurements. The user (farmer or aquaponics practitioner) can get real-time insights regarding the farm's performance with this information. It can also be used for the creation of future smart applications that can perform and showcase short and long-term predictions.
引用
收藏
页码:168 / 172
页数:5
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