Development and characterization of a highly sensitive fluorometric transducer for ultra low aqueous ammonia nitrogen measurements in aquaculture

Wang, Cong; Li, Zhen; Pan, Zhongli; Li, Bingbing

doi:10.1016/j.compag.2018.05.011

Cited by 30 publications

(7 citation statements)

References 42 publications

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“…On the other hand, the sensor circuit captures the sensor's signal, amplifies, filters, and conditions it to ensure it is ready for digitization and transmission to the IoT network. 57,[154][155][156] Additionally, the computing system, functioning as a System-on-Chip (SoC), plays a pivotal role. It not only facilitates sensor actuation if required but also processes data from the sensor circuit, prepares data for the IoT network, and establishes communication with a wireless modem to create the IoT network.…”

Section: Electronic System To Integrate N Sensormentioning

confidence: 99%

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Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Shrestha,

Wei

2024

J. Electrochem. Soc.

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Smart agriculture (SA) based on the framework of precision agriculture (PA) is a vital component of sustainable and efficient food production, with nitrogen (N) management playing a pivotal role. However, existing agricultural practices often suffer from low nitrogen use efficiency (NUE), posing a challenge to SA. To tackle this issue, real-time N sensing technologies offer farmers precise and timely information about soil N levels, enabling precise N fertilizer application. Integrating these technologies with the Internet of Things (IoT) can further augment their capabilities, creating a seamless platform for data collection, analysis, and decision-making for great opportunities to improve NUE. Nevertheless, the adoption of real-time N sensing and IoT integration also presents several challenges, including selecting appropriate sensing technologies, effective data mining and management, and acquiring specialized knowledge and training. This review paper provides a comprehensive analysis of the opportunities and challenges associated with real-time N sensing technologies and IoT integration in smart farming. By showcasing best practices and innovative solutions, the paper aims to foster widespread adoption of SA practices, ultimately elevating the sustainability and productivity of agricultural systems.

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Section: Electronic System To Integrate N Sensormentioning

confidence: 99%

“…The amplification stage for optical sensors 57,[154][155][156] typically involves a transimpedance amplifier, a high-gain amplifier that converts small changes in light intensity into a voltage signal. This signal can be further amplified and filtered.…”

Section: Electronic System To Integrate N Sensormentioning

confidence: 99%

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Shrestha,

Wei

2024

J. Electrochem. Soc.

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“…In order to obtain these parameters, the produced ammonia must be quantified, 44 and therefore accurate, sensitive and reproducible ammonia quantification methods are required. Several ammonia quantification methods are available nowadays, 45,46 the most widely used ones are spectrophotometric (i.e. colorimetric) methods, 47,48 ammonia ion-selective based methods, 49 biosensors, 50 ion chromatography, 51,52 Fourier Transform Infrared Radiation (FTIR) spectroscopy, 53 and 1 H NMR spectroscopy.…”

Section: List Of Symbolsmentioning

confidence: 99%

Alkali Metal Salt Interference on the Salicylate Method for Quantifying Ammonia from Nitrogen Reduction

et al. 2022

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The salicylate method has been extensively used for quantifying ammonia in the emerging field of nitrogen (electro)fixation. Alkali metal salts are widely used as supporting electrolytes for nitrogen reduction, especially in the context of electrochemical nitrogen fixation. However, these salts are known to cause interferences on the salicylate method, introducing significant uncertainties in ammonia quantification. In this work, the interference of lithium, sodium and potassium chlorides, perchlorates and sulfates on the ammonia quantification results obtained using the salicylate method was experimentally quantified, and an empirical model was developed to capture the effect of the presence of these interferents on the ammonia quantification by the salicylate method. Based on the obtained experimental interference results, the tested interferents can be ranked from stronger interferent (i.e. lower admissible concentration) to weaker interferent: Li2SO4, KClO4, LiCl, LiClO4, K2SO4, NaClO4, NaCl, Na2SO4, KCl. The developed model can be used to assess the experimental error in ammonia quantification from nitrogen reduction, in samples containing these interferents. This model can be used to correct the interferences on the ammonia quantification, when the interferent concentration in a sample is known (or measurable).

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“…We analyze information including pH, dissolved oxygen, temperature, nitrogen compounds, electric conductivity, alkalinity, and meteorological data [ 7 , 8 ], along with ML techniques that deal with small datasets. It is known that nitrogen compounds, electric conductivity, and alkalinity are correlated with the toxicity of the water pond, the presence of harmful ions, or the impact of pH on the water quality of the pond [ 5 , 9 ]. Hence, we not only study the accuracy of the models but also the importance of the variables to conduct the estimation and forecasting process.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for manually-measured water quality prediction in fish farming

et al. 2021

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Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture has focused on scenarios where devices for real-time data acquisition, storage, and remote monitoring are available, making it easy to develop accurate ML techniques. However, fish farmers do not necessarily have access to such devices. Many of them prefer to use equipment to manually measure these variables limiting the amount of available data to process. In this work, we study the use of random forests, multivariate linear regression, and artificial neural networks in scenarios with limited amount of measurements to analyze data from water-quality variables that are commonly measured in fish farming. We propose a methodology to build models in two scenarios: i) estimation of unobserved variables based on the observed ones, and ii) forecasting when a low amount of data is available for training. We show that random forests can be used to forecast dissolved oxygen, pond temperature, pH, ammonia, and ammonium when the water pond variables are measured only twice per day. Moreover, we showed that these prediction models can be implemented on a mobile-based information system and run in an average smartphone that fish farmers can afford.

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Development and characterization of a highly sensitive fluorometric transducer for ultra low aqueous ammonia nitrogen measurements in aquaculture

Cited by 30 publications

References 42 publications

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Review—Perspectives on the Roles of Real time Nitrogen Sensing and IoT Integration in Smart Agriculture

Alkali Metal Salt Interference on the Salicylate Method for Quantifying Ammonia from Nitrogen Reduction

Machine learning for manually-measured water quality prediction in fish farming

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