An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms

Gao, Guandong; Xiao, Ke; Chen, Miao-Miao

doi:10.1016/j.compag.2019.105013

Cited by 89 publications

(51 citation statements)

References 22 publications

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“…Gao et al [ 52 ] studied IoT-based intelligent fish farming and tracking control systems to assist in long-term analysis and improved decision-making. Furthermore, they suggested an intelligent management module including integrated sensor assembly, data acquisition and database management, data analysis and optimization, a graphical user interface (GUI) for handling queries, and a control actuator.…”

Section: Discussionmentioning

confidence: 99%

“…The query and management of the GUI makes it possible for farmers to see charts showing fishpond-monitoring data, configure aquaculture management modes, and perform manual control if required [ 52 ]. Gao et al [ 52 ] further provided two types of GUI interfaces—a mobile app interface and WeChat interface—to enable the remote viewing and management of information.…”

Section: Discussionmentioning

confidence: 99%

“…An IoT-based automation system can regularly and efficiently monitor various critical parameters in fish farming, such as the water level, temperature, and dissolved oxygen [ 52 ]. IoT technologies expedite the tracking of farm products all the way to their destination, which is ideal for farm products that require further processing, as buyers can know all the information in advance [ 53 ].…”

Section: Background and Methodsmentioning

confidence: 99%

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IoT Platform for Seafood Farmers and Consumers

Jæger

Mishra

2020

Sensors

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There has been a strong growth in aquatic products supported by the global seafood industry. Consumers demand information transparency to support informed decisions and to verify nutrition, food safety, and sustainable operations. Supporting these needs rests on the existence of interoperable Internet of Things (IoT) platforms for traceability that goes beyond the minimum “one up, one down” scheme required by regulators. Seafood farmers, being the source of both food and food-information, are critical to achieving the needed transparency. Traditionally, seafood farmers carry the costs of providing information, while downstream actors reap the benefits, causing limited provision of information. Now, global standards for labelling, data from IoT devices, and the reciprocity of utility from collecting data while sharing them represent great potential for farmers to generate value from traceability systems. To enable this, farmers need an IoT platform integrated with other IoT platforms in the value network. This paper presents a case study of an enterprise-level IoT platform for seafood farmers that satisfies consumers’ end-to-end traceability needs while extracting data from requests for information from downstream actors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Background and Methodsmentioning

confidence: 99%

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IoT Platform for Seafood Farmers and Consumers

Jæger

Mishra

2020

Sensors

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“…Furthermore, the different environmental factors (e.g., SD, Temp, pH, PO 4 -P, DO, and NH 4 -N) showed significant differences in terms of their impact on potential pathogens [24]. Therefore, balancing and rationally regulating the aquaculture water quality indicators is particularly important for farmers [80][81][82], which also provides important clues for the prevention of aquaculture diseases. The filtered OTU table of potential pathogenic bacteria of PC and GC groups are provided in Supplementary material Table S2.…”

Section: Correlation Analyses Of Environmental Factorsmentioning

confidence: 99%

A First Insight into the Structural and Functional Comparison of Environmental Microbiota in Freshwater Turtle Chinemys reevesii at Different Growth Stages under Pond and Greenhouse Cultivation

et al. 2020

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The microbial community structure of water is an important indicator for evaluating the water quality of the aquaculture environment. In this study, the investigation and comparison of the bacterial communities of pond cultivation (PC) and greenhouse cultivation (GC) between hatchling, juvenile, and adult growth stages of C. reevesii were performed. In addition, the V4 regions of the 16S rRNA gene were sequenced. The Chao1 richness estimator of the PC group was significantly higher than that of the GC group. The beta diversity showed that the microbiotas of the two groups were isolated from each other. The dominant phyla were Cyanobacteria, Proteobacteria, Actinobacteria, Bacteroidetes, Verrucomicrobia, and Planctomycetes in the PC group and Proteobacteria, Bacteroidetes, Firmicutes, Cyanobacteria, Chloroflexi, and Actinobacteria in the GC group. Both the numbers and the types of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations differed between the PC and GC groups. The prediction of bacterial phenotype implied that the GC environment is more likely to deteriorate, and turtles are more susceptible to pathogens than those of the PC environment. In addition, a total of nine potential pathogenic bacteria were identified and the correlation of environmental factors analyses showed significant differences of bacterial species between the PC and GC groups, while the potential pathogenic bacteria showed significant correlation with the stocking density, temperature, pH, orthophosphate (PO4-P), and dissolved oxygen (DO) in both the PC and GC groups. Noticeably, this is the first report to describe the different microbiota characteristics of the different cultivation environments in the different growth stages of C. reevesii, which will provide valuable data for water quality adjustment, disease prevention, and the healthy breeding of turtles.

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“…Manajemen kualitas air yang cerdas berbasis IoT telah direalisasikan dengan membuat prediksi kualitas air berdasarkan data historis. Dengan adanya fitur prediksi ini, maka kualitas air yang abnormal di masa mendatang dapat diantisipasi dan dijaga pada rentang ideal [20]. Pada penelitian [21], telah dirancang sistem pengontrol air untuk mengurangi masalah pemborosan air dengan memanfaatkan teknologi IoT.…”

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Rancang Bangun Sistem Pakar Pemantau Kualitas Air Berbasis IoT Menggunakan Fuzzy Classifier

Ramadhan

Dewantoro

Setiaji

2020

Jurnal Teknik Elektro

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The classification of water quality is vital to ensure that the water has been properly utilized. As of today, the water treatment plant employs a conventional method by taking water sample, measuring all of water quality parameters, and analyzing each sample. Besides, the conclusion-drawing processes have not been incorporated which might lead to water quality misclassification and prolonged efforts. In this study, an expert system was developed to monitor the water quality in real time fashion, therefore it could be accessed anytime and anywhere. The water quality analysis process was conducted by means of fuzzy classifier, and implemented on Arduino Mega 2560 board. The fuzzy inputs included pH value, total dissolved solid (TDS), and turbidity. A fuzzy inference system was employed to classify the water quality into three classes, namely good (meet the hygiene standards), fair, and poor (polluted). The expert system successfully yielded the inference results with a success rate of 100%. The water quality monitoring and classification could be accessed online through Internet of Things (IoT) platform Thingspeak.

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An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms

Cited by 89 publications

References 22 publications

IoT Platform for Seafood Farmers and Consumers

IoT Platform for Seafood Farmers and Consumers

A First Insight into the Structural and Functional Comparison of Environmental Microbiota in Freshwater Turtle Chinemys reevesii at Different Growth Stages under Pond and Greenhouse Cultivation

Rancang Bangun Sistem Pakar Pemantau Kualitas Air Berbasis IoT Menggunakan Fuzzy Classifier

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