The real time monitoring of water quality in IoT environment

Vijayakumar, Nisha; Ramya, R.

doi:10.1109/iccpct.2015.7159459

Cited by 44 publications

(24 citation statements)

References 9 publications

(2 reference statements)

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“…(76) A few other related works have been reported. (77,78) Studies should also be carried out on the use of hyperspectral images to find the real-time status of water quality. Recently, there has been skyrocketing growth in the study and experiments on water quality estimation using ML and RS techniques.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Wagle¹,

Acharya²,

Lee³

2020

Sensors and Materials

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Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unplanned urbanization, rapid industrialization, and uncontrollable human intervention, water quality is gradually degrading. This affects not only marine animals but also humans. Thus, the quality of water should be examined regularly. Water quality parameters should be estimated to monitor water quality. In general, water quality parameters are measured by in situ measurements. Although these measurements are accurate, they are costly and do not provide real-time spatial and temporal changes in water quality. To overcome this limitation, water quality parameters can be estimated using machine learning (ML) along with remote sensing (RS) data. A combination of ML and RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters. In this paper, some articles based on this approach are reviewed. By analyzing the literature, it was found that the integrated use of RS-based geospatial data with ML helps to produce an accurate result. Most of the authors used the regression algorithm in the estimation of the water quality parameters, with a support vector machine (SVM) regression intensively used. The artificial neural network (ANN) algorithm was the most used algorithm of ML in many of the studies. The researchers used multispectral images for their study. By applying ML to RS data, water quality monitoring systems are evolving into real-time artificial intelligence (AI)-enabled models that provide valuable recommendations and insights to support farmers to make decisions and take action in aquaculture.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Wagle¹,

Acharya²,

Lee³

2020

Sensors and Materials

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“…The livestock monitoring items include information such as body temperature, weight, behavior, exercise volume, food intake, disease information and environmental factors, which can help people understand animal’s own physiological and nutritional status and adaptability to external environmental conditions. In aquaculture, management projects focus on water quality such as dissolved oxygen content, water temperature and pH value [103,104] because water quality greatly affects the growth of aquatic animals. With animal growth and nutrient optimization model and intelligent IoT equipment, it is possible to realiaze automatic feeding and optimal control of feeding time and intake according to their growth cycle, individual quality, feeding cycle and eating situation [105,106].…”

Section: Iot Applications In Protected Agriculturementioning

confidence: 99%

State-of-the-Art Internet of Things in Protected Agriculture

Shi

Zhao

et al. 2019

Sensors

247

125

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The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future.

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“…(6) Chemical sensor IoT related chemical sensors, which are capable of tracking chemical pollution in manufacturing facilities, create synergy between areas of analytical techniques, devices, and machinery, sensing systems as well as information technology and network systems [162,163] .…”

Section: Teng-based Sensors and Sensing Systemsmentioning

confidence: 99%

Triboelectric nanogenerators enabled internet of things: A survey

Dharmasena

et al. 2020

Intell. and Converged Netw.

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The real time monitoring of water quality in IoT environment

Cited by 44 publications

References 9 publications

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

State-of-the-Art Internet of Things in Protected Agriculture

Triboelectric nanogenerators enabled internet of things: A survey

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