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用于水产养殖的基于物联网的高效实时水质监测方法

IoT-enabled effective real-time water quality monitoring method for aquaculture.

作者信息

Shete Rupali P, Bongale Anupkumar M, Dharrao Deepak

机构信息

Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune Campus, Lavale, Pune, Maharashtra, India.

Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune Campus, Lavale, Pune, Maharashtra, India.

出版信息

MethodsX. 2024 Aug 13;13:102906. doi: 10.1016/j.mex.2024.102906. eCollection 2024 Dec.

DOI:10.1016/j.mex.2024.102906
PMID:39263361
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11387385/
Abstract

Aquaculture is growing industry from the perspective of sustainable food fulfillment and county's economic development. Technology oriented aquafarming is the solution for effective water quality monitoring and high yield production. Internet of Things (IoT) integrated aquaculture can cater to such requirements. This research article introduces a comprehensive method aimed at seamlessly incorporate IoT sensors into aquafarming environments, utilizing Arduino boards and communication modules. The proposed method measures accurate water quality parameters, such as temperature, pH levels, and Dissolved Oxygen (DO), which are essential for maintaining optimal conditions for suitable aquaculture environment. This method enables the real-time collection of critical data points that are essential prevent fish diseases and mortality with low human intervention and maintenance cost. The key contributions of the methodology are mentioned below.•Design and development of a compact and efficient Printed Circuit Board (PCB) to achieve accurate sensor data readings and reliable communication in an aqua environment.•Prevent fish disease and mortality rate through data-driven decision incorporating correlation of DO, pH, and temperature sensor data.•Conducted instrument calibration checks and cross-validated automated system data with manual observations through repeatability tests to ensure precise measurements of sensor parameters.

摘要

从可持续粮食供应和国家经济发展的角度来看,水产养殖是一个不断发展的产业。以技术为导向的水产养殖是有效水质监测和高产的解决方案。物联网(IoT)集成水产养殖可以满足此类要求。本文介绍了一种全面的方法,旨在将物联网传感器无缝集成到水产养殖环境中,利用 Arduino 板和通信模块。所提出的方法可测量准确的水质参数,如温度、pH 值和溶解氧(DO),这些参数对于维持适宜水产养殖环境的最佳条件至关重要。该方法能够实时收集关键数据点,这些数据点对于以低人工干预和维护成本预防鱼类疾病和死亡率至关重要。该方法的关键贡献如下:

•设计并开发一个紧凑高效的印刷电路板(PCB),以在水产环境中实现准确的传感器数据读取和可靠通信。

•通过纳入溶解氧、pH 值和温度传感器数据的相关性,利用数据驱动的决策预防鱼类疾病和死亡率。

•进行仪器校准检查,并通过重复性测试将自动化系统数据与人工观测进行交叉验证,以确保传感器参数的精确测量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/1e1955e126f6/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/9f83c5fb7e59/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/b3d2c925a0c7/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/c604ce0ac307/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/be1319f0fd1d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/1e1955e126f6/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/9f83c5fb7e59/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/b3d2c925a0c7/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/c604ce0ac307/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/be1319f0fd1d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbbc/11387385/1e1955e126f6/gr8.jpg

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PLoS One. 2022 Jun 22;17(6):e0270202. doi: 10.1371/journal.pone.0270202. eCollection 2022.
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State of the Art Techniques for Water Quality Monitoring Systems for Fish Ponds Using IoT and Underwater Sensors: A Review.基于物联网和水下传感器的鱼塘水质监测系统的最新技术:综述。
Sensors (Basel). 2022 Mar 8;22(6):2088. doi: 10.3390/s22062088.