Empowered edge intelligent aquaculture with lightweight Kubernetes and GPU-embedded

Fathoni, Halim; Yang, Chao-Tung; Huang, Chin-Yin; Chen, Chien-Yi

doi:10.1007/s11276-023-03592-2

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The future of aquaculture food production needs to face a lot of challenges among them the preservation of water quality, cost effectiveness, food quality and space utility [99], but one of the most important is waste compound usage which is important to transform the system into a sustainable production method [130]. Nowadays, different and new technologies are helping to transform aquaculture food production with the use of sensors, artificial intelligent models [132] or the introduction of the Internet of Thinks to make production management easier [133]. Wastewater treatment is one of the most important aspects and the future lies in the capacity to remove a high quantity of residues by using different biofilter and recirculation systems [134,135] with different plants and aquatic animals to evade damaging nutrients discharges into the environment [118].…”

Section: Groundwater Usage and The Environmental Impact In Aquacultur...mentioning

confidence: 99%

Horticulture Irrigation Systems and Aquaculture Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint

Schoor,

Arenas-Salazar,

Parra-Pacheco

et al. 2024

Preprint

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The expansion of food production is getting more important due to a rising world population, which is relying on food security on a regional and local scale. Intensive food production systems create a negative impact on the regional ecosystem because of agrochemical pollution and nutrient rich water discharges into nearby rivers. Furthermore, these systems are highly depending on regional water resources causing water scarcity and soil erosion due to the overexploitation of natural resources in general. The objective of this article is to review the water usage in the two most water intensive food production systems, agriculture and aquaculture showing lacking areas, like system management and climate change, which must be considered in the implementation of sustainable water footprint. In addition, the review includes an analysis if the combination of both production system into aquaponic food production and the possibilities of water saving. There are a variety of water footprint analyses for crop and aquatic animal production, but there is also a lack of information about the system management including irrigation systems, system cleaning processes, water substitution, pond removal, evaporation due to climate change and especially in aquaculture, the water footprint of industrial elaborated fish feed.

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Section: Groundwater Usage and The Environmental Impact In Aquacultur...mentioning

confidence: 99%

Horticulture Irrigation Systems and Aquaculture Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint

Schoor,

Arenas-Salazar,

Parra-Pacheco

et al. 2024

Preprint

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“…Edge AI includes not only individual devices, but also computing nodes or servers strategically located near data sources. In this study, the authors explore the use of edge computing in an intelligent aquaculture system [83]. They implemented a heterogeneous architecture consisting of various devices such as NVIDIA Jetson Nx and Jetson Nano to collect water sensor data and to perform real-time video-based fish detection using DL.…”

Section: Edge Intelligencementioning

confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

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Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

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“…The future of aquacultural food production needs to face a lot of challenges, including the preservation of water quality, cost effectiveness, food quality, and space utility [99], but one of the most important is waste compound usage, which is important to transform the system into a sustainable production method [129]. Nowadays, different and new technologies are helping transform aquacultural food production with the use of sensors, artificial intelligent models [131], or the introduction of the Internet of Things to make production management easier [132]. Wastewater treatment is one of the most important aspects, and the future lies in the capacity to remove a high quantity of residues using different biofilter and recirculation systems [133,134] with different plants and aquatic animals to avoid damaging nutrient discharged into the environment [117].…”

Section: Groundwater Usage and The Environmental Impact In Aquacultur...mentioning

confidence: 99%

Horticultural Irrigation Systems and Aquacultural Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint

Schoor,

Arenas-Salazar,

Parra-Pacheco

et al. 2024

Agriculture

View full text Add to dashboard Cite

The expansion of food production is becoming more important due to a rising world population, which is relying on food security on regional and local scales. Intensive food production systems exert a negative impact on the regional ecosystem because of agrochemical pollution and nutrient-rich water discharging into nearby rivers. Furthermore, these systems highly depend on regional water resources, causing water scarcity and soil erosion due to the overexploitation of natural resources in general. The objective of this article is to review the water usage in the two most water-intensive food production systems, agriculture and aquaculture, showing lacking areas like system management and climate change, which must be considered in the implementation of a sustainable water footprint. In addition, the review includes an analysis of the combination of both production systems in aquaponic food production and the possibilities of water saving. There are a variety of analyses related to water usage for crop and aquatic animal production, but in these analyses, there is a lack of information about system management in general, which includes cleaning processes, water substitution, pond removal, water evaporation, and, especially in aquaculture, the water usage required for industrially elaborated fish feed.

show abstract

Empowered edge intelligent aquaculture with lightweight Kubernetes and GPU-embedded

Cited by 3 publications

References 22 publications

Horticulture Irrigation Systems and Aquaculture Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint

Horticulture Irrigation Systems and Aquaculture Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Horticultural Irrigation Systems and Aquacultural Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint

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