Water IoT Monitoring System for Aquaponics Health and Fishery Applications

Alselek, Mohammad; Calero, José M. Alcaraz; Segura-García, Jaume; Wang, Qi

doi:10.3390/s22197679

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…The key indicators of bacterial activity are the ammonia and nitrite levels; they should always be under 1 mg/L in a balanced system [17]. IoT sensors can monitor the ammonia and nitrate levels, identify changes in the nitrification rate, and ensure they remain within their optimal ranges [21].…”

Section: Optimising Filtration and Bioconversionmentioning

confidence: 99%

Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities

Channa,

Munir,

Hansen

et al. 2024

Encyclopedia

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Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies.

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Section: Optimising Filtration and Bioconversionmentioning

confidence: 99%

Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities

Channa,

Munir,

Hansen

et al. 2024

Encyclopedia

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show abstract

“…Further, Haryanto et al [ 16 ] designed a smart aquaponics system that could control and monitor the degree of acidity, water level, water temperature, and fish feed that were integrated with an internet-based mobile application. Mohammad Alselek et al [ 17 ] developed a complete 5G-enabled IoT system for fully monitoring the performance of fishery farms, which improves the state-of-the-art in terms of aquaponics life cycle monitoring metrics and communication technologies. Mehra et al [ 18 ] introduced deep neural networks to the analysis of tomato hydroponic growth parameters and compared them with traditional soil culture.…”

Section: Introductionmentioning

confidence: 99%

A Modularized IoT Monitoring System with Edge-Computing for Aquaponics

Wan

Zhao

et al. 2022

Sensors

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Aquaponics is a green and efficient agricultural production model that combines aquaculture and vegetable cultivation. It is worth looking into optimizing the proportion of fish and plants to improve the quality and yield. However, there is little non-destructive monitoring of plant growth in aquaponics monitoring systems currently. In this paper, based on the Internet of Things technologies, a monitoring system is designed with miniaturization, modularization, and low-cost features for cultivation-breeding ratio research. The system can realize remote monitoring and intelligent control of parameters needed to keep fish and plants under optimal conditions. First, a 32-bit chip is used as the Microcontroller Unit to develop the intelligent sensing unit, which can realize 16 different data acquisitions as stand-alone extensible modules. Second, to achieve plant data acquisition and upload, the Raspberry Pi embedded with image processing algorithms is introduced to realize edge-computing. Finally, all the collected data is stored in the Ali-cloud through Wi-Fi and a WeChat Mini Program is designed to display data and control devices. The results show that there is no packet loss within 90 m for wireless transmission, and the error rate of environment parameters is limited to 5%. It was proven that the system is intelligent, flexible, low-cost, and stable which is suitable for small-scale aquaponics well.

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“…Additionally, the article proposes a modeling framework for large-scale urban agriculture ecosystems, forming the foundation for a decision support system that coordinates activities among independent producers to achieve collective goals. Another work related to aquaponics is [18], where the authors propose and deploy a whole efficient system for the feeding cycle in fish farms and the generation of plant nutrients. This system has proven to provide good aquaponics life-cycle monitoring metrics with different communication technologies and has also allowed a reduction in energy consumption to make the system more sustainable.…”

mentioning

confidence: 99%

Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools

Catala-Roman,

Navarro,

Segura-Garcia

et al. 2024

Applied Sciences

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Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects.

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Water IoT Monitoring System for Aquaponics Health and Fishery Applications

Cited by 8 publications

References 9 publications

Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities

Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities

A Modularized IoT Monitoring System with Edge-Computing for Aquaponics

Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools

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