Adaptive predictive control of dissolved oxygen concentration in a dynamic model of whiteleg shrimp culture

Sacasqui, Marcos; Sánchez, Ismael; Vásquez, Edilberto

doi:10.1109/chilecon.2017.8229499

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The combined work of sensors, ML and AI can be utilised to move towards the decision‐making process easily using intelligent devices such as aerators and feeders. Dissolved oxygen sensors integrated with the controller have been utilised to maintain the oxygen level in whiteleg shrimp culture using adaptive predictive control 81 . ML has also been employed to separate dead embryos from live ones to prevent fungal infection in rainbow trout.…”

Section: Early Diagnosis and Monitoring Methodsmentioning

confidence: 99%

“…Dissolved oxygen sensors integrated with the controller have been utilised to maintain the oxygen level in whiteleg shrimp culture using adaptive predictive control. 81 ML has also been employed to separate dead embryos from live ones to prevent fungal infection in rainbow trout. This ML approach resulted in an average accuracy of 99.45%, suggesting its possible use in commercial aquaculture.…”

Section: Artificial Intelligence (Ai) and Machine Learning (Ml)mentioning

confidence: 99%

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Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health

Bohara,

Joshi,

Acharya

et al. 2023

Reviews in Aquaculture

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In recent years, aquaculture has seen tremendous growth worldwide due to technological advancements, leading to research and development of various innovations. Aquaculture farmers prioritise early diagnosis for timely treatment to achieve better productive and economic performance. Aquatic animal health experts still employ traditional diagnostic methods using visual diagnosis, cell culture, media culture, histopathology and serology. However, the developments of technologies in aquamedicine, such as sequencing, biosensors and CRISPR, have enabled rapid disease detection within minutes. Furthermore, integrating sensors, drones, artificial intelligence and the internet in aquaculture farm monitoring has helped farmers take decisive actions to improve production. Advancements in diagnostic techniques have significantly enhanced the efficient detection of bacterial, viral, parasitic and fungal diseases in aquatic animals. Moreover, monitoring water quality, aquatic animal health and animal behaviour on farms has become exceptionally streamlined with cutting‐edge tools like drones, sensors and artificial intelligence. Summarising research and development in aquatic animal health and monitoring aids efficient technology adoption in aquaculture. With these advanced technologies' continued development and adoption in developed countries, the aquaculture industry is experiencing growth and increased efficiency, benefiting farmers and consumers in these regions. However, farmers and educators in developing countries lack information about these technologies. Training of agricultural educators and efficient dissemination of knowledge and technologies through advertising and publication in collaboration with companies is essential. This review delves into emerging technologies capable of replacing the conventional diagnostic and monitoring methods utilised in aquaculture. We also explore their strengths, limitations and potential future applications within aquaculture settings.

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Section: Early Diagnosis and Monitoring Methodsmentioning

confidence: 99%

Section: Artificial Intelligence (Ai) and Machine Learning (Ml)mentioning

confidence: 99%

Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health

Bohara,

Joshi,

Acharya

et al. 2023

Reviews in Aquaculture

View full text Add to dashboard Cite

show abstract

“…The predicted value is used as the feedback value of the variable universe fuzzy PID controller to realize the predictive control and advanced regulation of DO. Another predictive controller was developed for hourly control DO to improve the production of whiteleg shrimp and minimize costs; Marcos et al (2017) first established a DO dynamic model by analyzing the process of DO production and consumption in shrimp ponds and the growth rate of shrimp. Then, the Nonlinear Extended Prediction Self Adaptive Control (NEPSAC) algorithm is applied to the dynamic model to optimize the use of the aeration system.…”

Section: Water Quality Soft Measurement and Control Methodsmentioning

confidence: 99%

Intelligent fish farm—the future of aquaculture

Wang

et al. 2021

Aquacult Int

112

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With the continuous expansion of aquaculture scale and density, contemporary aquaculture methods have been forced to overproduce resulting in the accelerated imbalance rate of water environment, the frequent occurrence of fish diseases, and the decline of aquatic product quality. Moreover, due to the fact that the average age profile of agricultural workers in many parts of the world are on the higher side, fishery production will face the dilemma of shortage of labor, and aquaculture methods are in urgent need of change. Modern information technology has gradually penetrated into various fields of agriculture, and the concept of intelligent fish farm has also begun to take shape. The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting through the idea of “replacing human with machine,” so as to liberate the manpower completely and realize the green and sustainable aquaculture. This paper reviews the application of fishery intelligent equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern aquaculture, and analyzes the existing problems and future development prospects. Meanwhile, based on different business requirements, the design frameworks for key functional modules in the construction of intelligent fish farm are proposed.

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“…The model can be integrated into population dynamics models to simulate the biomass and density throughout the stages of the shrimp life cycle, which may be usefully applied both to penaeid aquaculture and to the management of wild fisheries. In another study, Sacasqui et al (2017) (ID.3, Table 2) simulated dissolved oxygen levels in the shrimp ponds with the key input of water temperature, and its impact on photosynthesis, respiration and degradation of organic matter.…”

Section: Dynamic Simulation Models For Shrimp Aquaculturementioning

confidence: 99%

Dynamic modelling of coastal aquaculture systems: A Review

Muralidhar,

Kumar,

Suvana

et al. 2023

Aquatic Ecosystem Health &Amp; Management

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Coastal aquaculture is an important economic activity in India dominated majorly by shrimp culture, which involves a range of interconnected processes that are challenging to analyse and optimise without a systematic approach. System dynamics modelling is a useful tool for understanding and predicting the behavior of complex coastal aquaculture systems. Here, we review the status of dynamic simulation modelling works undertaken in aquaculture, which can provide directives for various researchers working on developing simulation models for shrimp aquaculture. There is a need to assess the impact of dynamic forces on the animals during the culture period which could be addressed through these models. System dynamic models assist decision-makers to augment potential measures for aquaculture-related problems under different possible scenarios. System dynamic models developed in aquaculture were related to feeding, water quality parameters, nitrogen dynamics, growth, etc. The strengths and limitations of software packages used in developing the simulation models are discussed. Considering the economic potential of shrimp aquaculture, it is important to develop an integrated dynamic model for predicting all the sub-processes of shrimp aquaculture.

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Adaptive predictive control of dissolved oxygen concentration in a dynamic model of whiteleg shrimp culture

Cited by 5 publications

References 12 publications

Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health

Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health

Intelligent fish farm—the future of aquaculture

Dynamic modelling of coastal aquaculture systems: A Review

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