Continuous and remote monitoring of ground water level measurement in a well

Mary, Xavier; Rose, Lina; Rajasekaran, Karthik

doi:10.1504/ijw.2018.095397

Cited by 5 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Fusarium head blight is a harmful parasite, since it releases deoxynivalenol within kernels, which is toxic for humans [17]. We managed to refine an algorithm whose skeleton is similar to the downy mildew one, while also taking into account rainfall (whose intensity should be measured with state-of-the-art sensors [18] [19]) and dishomogeneities in T and U. We are now in the testing stage, and we are obtaining some encouraging results in confronting the algorithm's output with historical data.…”

Section: Discussionmentioning

confidence: 99%

Introducing Evja - "Rugged" Intelligent Support System for precision farming

Loret

Affinito²,

Bonanomi

2020

ACTA IMEKO

View full text Add to dashboard Cite

Precision agriculture is a farming system based on the combination of detailed observations, measuring and rapid-response to optimize energetic input to maximize crops production. Precision agriculture use decision support system (DSS) for optimize farm management. In this context, EVJA Observe Prevent Improve (or just EVJA) is an Intelligent Support System for precision agriculture. A vast set of data (i.e. temperature, relative humidity, deficit of vapour pressure, leaf wetness, solar radiation, carbon dioxide concentration, soil moisture etc.) is continuously collected, submitted to a local control unit, and processed through algorithms specifically developed for different crops. On the other hands, farmers can access EVJA from their pc and mobile devices, and monitor complex agronomic data analysis presented in a user-friendly interface.In this article, we will show how EVJA works, and how its output can be used to assess the health state of plants through a specific set of functions. Moreover, we will show the methodology to develop useful predictive models based on this information.Specifically, we will describe a predictive algorithms capable to predict the infection risks of downy mildew for baby leaves plantations and for Fusarium ear blight of wheat.

show abstract

Section: Discussionmentioning

confidence: 99%

Introducing Evja - "Rugged" Intelligent Support System for precision farming

Loret

Affinito²,

Bonanomi

2020

ACTA IMEKO

View full text Add to dashboard Cite

show abstract

“…The projected framework is SN and IoT based deployment to adjust values of climate parameters. The deployed SN is power efficient, optimized in volume, multi-functional and low cost (31)(32)(33) . The sensors are connected to a single node and placed randomly on desired locations for data collection that is further accelerated to the gateway.…”

Section: Contribution and Proposed System Modelmentioning

confidence: 99%

Intelligent greenhouse monitoring and control scheme: An arrangement of Sensors, Raspberry Pi based Embedded System and IoT platform

Arshad¹

2020

IJST

View full text Add to dashboard Cite

Objectives/ Methods: The conventional farming approaches have been found unable to deliver an appropriate quantity of fertilizer. Similarly, no explicit measure can be established to regulate the climate parameters. In this study, we have developed a prototype comprising a sensor network (SN) based node, Raspberry Pi based embedded system (ES) that is active to monitor the climatic parameters with air temperature, air humidity, soil moisture, air carbon dioxide, and light intensity within a greenhouse environment. Raspberry Pi based ES is integrated with internet-of-things (IoT) analytics, termed as ThingSpeak, and sensor nodes are physically placed in a greenhouse environment to record climate parameter data being forwarded to the gateway. The gateway nodes direct this data to the agriculture professional(s) through a web browser over the internet. Findings: Based on the received data, the ES triggers the intelligent decision-making by implementing appropriate arrangements to regulate climate parameters. For example, the greenhouse climate is controlled through switching``ON'' certain devices i.e., lights, water sprinklers/exhaust fan, and fan. We deliberate that the proposed scheme can be implementable at a bigger scale without any difficulty and it will be much advantageous for an increase in sustainability, productivity, and profitability of a farming system. Novelty: Up to the authors' best knowledge, it is the first wireless deployment of ThingSpeak analytics for a smart greenhouse environment by using the Raspberry Pi. The main advantages of this intelligent crop monitoring system include uninterrupted mobile monitoring of the greenhouse, improved crop yield and productivity, negligible human interface/dependency.

show abstract

“…It is also one of the most valuable unlimited natural resources in the world. Statistically, the freshwater resource is approximately about 4 % of the total water available on earth out of which the remaining 96 % is salty, in the seas and oceans [2]. Meanwhile, only about 0.001 % is available as a groundwater resource that is hidden underground, while 75 % is ice and about 25 % is liquid water [3].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to usage, as a result of high non-linear and non-stationary natures of groundwater resources in time series, its efficient management depends on various complex factors such as precipitation, groundwater aquifers, and other environmental factors [7]. Consequently, it is of vital importance that an effective management model for accurate groundwater level management model be developed [8,9] Measurement of groundwater level is important to avoid depletion of the groundwater resource as earlier stated [2]. Groundwater level measurement assists in determining the hydrological stress acting in an aquifer and provide data for efficient management.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Resources Management Modelling: A Review

Aderemi¹,

Olwal²,

Ndambuki³

et al. 2021

Preprint

View full text Add to dashboard Cite

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, this resource is not unlimited and its sustainability is highly dependent on its prudent use. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity and drought has become a major challenge for researchers as well as water managers. To solve these challenges, many solutions such as simulation and optimisation models have been proffered through the use of historical data. Therefore, this has made efficient data gathering essential to maintain data-driven groundwater level resource management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. Recently, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater level resource model have been revealed in the management of groundwater level resources. In addition, efficient data-driven groundwater level resource management relies hugely on information relating to changes in groundwater resource levels as well as its availability. At the moment, the groundwater managers lack an efficient and scalable groundwater management system to gather the required data. The literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater level resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management models, nonetheless, this paper seeks to highlight the current groundwater level resources management models as well as the IoT contributions.

show abstract

Continuous and remote monitoring of ground water level measurement in a well

Cited by 5 publications

References 0 publications

Introducing Evja - "Rugged" Intelligent Support System for precision farming

Introducing Evja - "Rugged" Intelligent Support System for precision farming

Intelligent greenhouse monitoring and control scheme: An arrangement of Sensors, Raspberry Pi based Embedded System and IoT platform

Groundwater Level Resources Management Modelling: A Review

Contact Info

Product

Resources

About