Water Color Identification System for Monitoring Aquaculture Farms

Chen, Hsiang-Chieh; Xu, Sheng-Yao; Deng, Kai-Han

doi:10.3390/s22197131

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…In experiment II, an offline digital image processing technique was used to analyze the captured image of the water color at fish farms, and the image segmentation method was applied to classify the objects in the image and extract the part to be tested. (19,20) Then, we could determine the RGB grayscale values in order to avoid errors due to human operation and the environment. (21) Here, MATLAB image processing functions were used.…”

Section: Methodsmentioning

confidence: 99%

Smart Water Quality Monitoring Technology for Fish Farms Using Cellphone Camera Sensor

Liu,

Chen,

Shih

et al. 2023

Sensors and Materials

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With the ever-changing nature of technology and the vigorous development of artificial intelligence, traditional fishery has gradually transformed into smart aquaculture. The current smart sensing instruments on the market can accurately measure water quality parameters for fish farms, but they are too expensive to be used popularly. On the basis of the mobile application developed by Taijiang Agriculture Biotechnology, a local company of Taiwan, and our empirical cumulative trend, we propose a smart technology for monitoring water quality in fish farms using a cellphone camera sensor. By the proposed method, we captured and recorded the RGB values of the water color using a cellphone camera sensor. At the same time, we measured the water quality parameters using a spectrophotometer and a Secchi disk at four fish farms. As a result of experimental design and analysis, we found an obvious correlation between water color and various water quality parameters. In the future, we expect to achieve the target that fishermen can use a cellphone to obtain real-time water quality information by utilizing the proposed low-cost and efficient aquaculture technology.

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Section: Methodsmentioning

confidence: 99%

Smart Water Quality Monitoring Technology for Fish Farms Using Cellphone Camera Sensor

Liu,

Chen,

Shih

et al. 2023

Sensors and Materials

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A critical review of irrigation water quality index and water quality management practices in micro-irrigation for efficient policy making

Anyango,

Bhowmick,

Sahoo Bhattacharya

2024

Desalination and Water Treatment

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Artificial intelligence methods used in various aquaculture applications: A systematic literature review

Aung,

Abdul Razak,

Rahiman Bin Md Nor

2024

J World Aquaculture Soc

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This research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

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Water Color Identification System for Monitoring Aquaculture Farms

Cited by 4 publications

References 39 publications

Smart Water Quality Monitoring Technology for Fish Farms Using Cellphone Camera Sensor

Smart Water Quality Monitoring Technology for Fish Farms Using Cellphone Camera Sensor

A critical review of irrigation water quality index and water quality management practices in micro-irrigation for efficient policy making

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

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