Wireless sensor network for aquaculture: Review, survey, and case study of aquaculture practices in western Godavari region

Shareef, Zeenat; Reddy, S. R. N.

doi:10.3233/ais-180498

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…Consequently, association between index quality and standard quality is provided by selecting a sequencing method that validates calibration and corroboration of sensors. Also, wireless sensors have been integrated, and their performance on water quality has been checked in the western region of Andhra Pradesh where direct field visits have been made for designing an autonomous mode of operation [ 2 ]. The researchers examined and created an autonomous vehicle that is capable of traveling inside water and reports corresponding information to the base station.…”

Section: Literature: a Brief Reviewmentioning

confidence: 99%

Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach

Kshirsagar

Manaoharan²,

Tirth

et al. 2022

Journal of Healthcare Engineering

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This article examines distinctive techniques for monitoring the condition of fishes in underwater and also provides tranquil procedures after catching the fishes. Once the fishes are hooked, two different techniques that are explicitly designed for smoking and drying are implemented for saving the time of fish suppliers. Existing methods do not focus on the optimization algorithms for solving this issue. When considering the optimization problem, the solution is adequate for any number of inputs at time t. For this combined new flanged technique, a precise system model has been designed and incorporated with a set of rules using contention protocols. In addition, the designed system is also instigated with a whale optimization algorithm that is having sufficient capability to test the different parameters of assimilated sensing devices using different sensors. Further to test the effectiveness of the proposed method, an online monitoring system has been presented that can monitor and in turn provides the consequences using a simulation model for better understanding. Moreover, after examining the simulation results under three different scenarios, it has been observed that the proposed method provides an enhancement in real-time monitoring systems for an average of 78%.

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Section: Literature: a Brief Reviewmentioning

confidence: 99%

Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach

Kshirsagar

Manaoharan²,

Tirth

et al. 2022

Journal of Healthcare Engineering

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“…So, an automated water quality monitoring system is needed to remove the manual water collection method to test and obtain real‐time water parameter values. This could be accomplished by using an internet‐based real‐time data acquisition method 7 . Internet‐based real‐time data help aqua farmers by enhancing management practices, profits and providing support for farming‐related issues in rural areas.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, when transferring water samples from the ponds to the laboratories, sometimes parameter (temperature and dissolved oxygen) readings can be changed slightly, and the lack of real time monitoring of water quality decreases the precision of the data. 7 As a result, farmers are unaware of worsening of the water quality in their ponds and are unable to prevent aquatic species from being susceptible to disease, resulting in significant losses. So, an automated water quality monitoring system is needed to remove the manual water collection method to test and obtain real-time water parameter values.…”

Section: Introductionmentioning

confidence: 99%

“…This could be accomplished by using an internet-based real-time data acquisition method. 7 Internet-based real-time data help aqua farmers by enhancing management practices, profits and providing support for farming-related issues in rural areas. The Internet of Things (IoT) in aquaculture practice has created opportunities for potential solutions for real-time monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Internet of Things (IoT)‐based aquaculture: An overview of IoT application on water quality monitoring

Prapti

Shariff

Man

et al. 2021

Reviews in Aquaculture

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Aquaculture based on the Internet of Things (IoT) is a growing field of interest in the fishing industry. The IoT technology is advancing the agriculture 4.0 era, and yet, Aquaculture 4.0 is a lagging field in many countries. This article presents results obtained so far from ongoing research of published work highlighting water quality monitoring in fishponds. This analysis was performed extensively from May to December 2020 by meticulously selecting a total of 30 internationally published research papers. This review is divided into five categories: (1) recent research (2011–2020), (2) aquaculture environments, (3) research approaches, (4) most common water quality parameters and (5) forms of the solution provided. Most of the published research concentrated on inland aquaculture (81%), while research articles on marine aquaculture species accounted for 19% of papers reviewed so far. The framework and architecture approach (48%) was the most widely practised research approach in IoT‐based aquaculture for water quality monitoring. There is a need for long‐term experimental research to identify the challenges and suggest appropriate solutions. With regards to water quality parameters, temperature (20%), dissolved oxygen (18%) and pH (17%) are the topmost prioritised water quality parameters considered in the IoT‐based aquaculture. Finally, real‐time monitoring (50%) is offered generally as a form of a solution while autonomous (3%) monitoring can be a unique solution. The findings from this study are expected to support the aquaculture industry, researchers, practitioners and decision‐makers in the long run.

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“…E-mail: rmartinez@ucam.edu. technologies to deal with several issues that affect agriculture [3][4][5]. Therefore, smart agriculture provides a real benefit to society in different ways, such as facilitating the stabilization and increase of agricultural production and improving the environmental impact of this activity [6].…”

Section: Introductionmentioning

confidence: 99%

A deep learning model to predict lower temperatures in agriculture

Guillén‐Navarro

Martínez‐España

Llanes

et al. 2020

AIS

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Deep learning techniques provide a novel framework for prediction and classification in decision-making procedures that are widely applied in different fields. Precision agriculture is one of these fields where the use of decision-making technologies provides better production with better costs and a greater benefit for farmers. This paper develops an intelligent framework based on a deep learning model for early prediction of crop frost to help farmers activate anti-frost techniques to save the crop. This model is based on a long short-term memory (LSTM) model and it is designed to predict low temperatures. The model is based on information from an IoT infrastructure deployed on two plots in Murcia (Southeast of Spain). Three experiments are performed; a cross validation to validate the model from the most pessimistic point of view, a validation of 24 consecutive hours of temperatures, in order to know 24 hours before the possible temperature drop and a comparison with two traditional time series prediction techniques, namely Auto Regressive Integrated Moving Average and the Gaussian process. The results obtained are satisfactory, being better the results of the LSTM, obtaining an average quadratic error of less than a Celsius degree and a determination coefficient R 2 greater than 0.95.

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Wireless sensor network for aquaculture: Review, survey, and case study of aquaculture practices in western Godavari region

Cited by 9 publications

References 46 publications

Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach

Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach

Internet of Things (IoT)‐based aquaculture: An overview of IoT application on water quality monitoring

A deep learning model to predict lower temperatures in agriculture

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