Design and Deployment of Low-Cost Sensors for Monitoring the Water Quality and Fish Behavior in Aquaculture Tanks during the Feeding Process

The monitoring of farming processes can optimize the use of resources and improve its sustainability and profitability. In fish farms, the water quality, tank environment, and fish behavior must be monitored. Wireless sensor networks (WSNs) are a promising option to perform this monitoring. Nevertheless, its high cost is slowing the expansion of its use. In this paper, we propose a set of sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process. The WSN is based on physical sensors, composed of simple electronic components. The system proposed can monitor water quality parameters, tank status, the feed falling and fish swimming depth and velocity. In addition, the system includes a smart algorithm to reduce the energy waste when sending the information from the node to the database. The system is composed of three nodes in each tank that send the information though the local area network to a database on the Internet and a smart algorithm that detects abnormal values and sends alarms when they happen. All the sensors are designed, calibrated, and deployed to ensure its suitability. The greatest efforts have been accomplished with the fish presence sensor. The total cost of the sensors and nodes for the proposed system is less than 90  .This work has been partially supported by the “Ministerio de Educación, Cultura y Deporte”, through the “Ayudas para contratos predoctorales de Formación del Profesorado Universitario FPU (Convocatoria 2014)”. Grant number FPU14/02953. The research leading to these results has received funding from “la Caixa” Foundation and Triptolemos Foundation

Study of Requirements and Design of Sensors for Monitoring Water Quality and Feeding Process in Fish Farms and Other Environments

Se están realizando muchos esfuerzos en la acuicultura para alcanzar la sostenibilidad, sin embargo aún está lejos de ser sostenible. Sus impactos sobre el medio ambiente pueden prevenirse y corregirse mediante el uso de sensores, desarrollando la conocida como acuicultura de precisión. La calidad del agua afecta el rendimiento de los peces. La temperatura y la salinidad son algunos factores que afectan al crecimiento de los peces. Sin embargo, otros factores como la turbidez, el fotoperíodo y el oxígeno disuelto entre otros pueden afectar a las necesidades nutritivas de los peces. Ajustar la cantidad de alimento necesario es crucial para garantizar la sostenibilidad de la acuicultura y para aumentar el beneficio económico de las instalaciones. Al monitorear la calidad del agua, es posible estimar las necesidades de alimentación. Sin embargo, no es suficiente. El monitoreo del comportamiento de los peces, especialmente durante el período de alimentación, puede ayudar a adaptar el alimento proporcionado. Entonces, si está tan claro que el monitoreo puede ayudar a la producción acuícola, ¿por qué no vemos este sistema de monitoreo en las instalaciones acuícolas? ¿Por qué en la mayoría de las instalaciones la alimentación se da manualmente sin considerar el comportamiento de alimentación de los peces? El precio de los sensores para monitorizar las piscifactorías es extremadamente alto. Los sensores empleados son, en la mayoría de los casos, los mismos que se utilizan para la oceanografía. Los sistemas propuestos en la literatura cubren pocos parámetros de calidad del agua y generalmente no consideran el comportamiento de alimentación de los peces. Son necesarios sensores de bajo costo adecuados para la monitorización de la acuicultura. Esos sensores deben ser de bajo costo, bajo consumo de energía, fáciles de usar y con la posibilidad de incluirlos en una red para enviar los datos. Por lo tanto, podremos utilizar esta red de sensores y sensores para controlar la actividad, enviar alarmas si es necesario y automatizar los procesos. Además, si incluimos Internet, los datos se pueden ver de forma remota. El uso de esos sensores puede ayudar a la producción acuícola. En esta tesis mostramos el estudio de los requisitos y el diseño de sensores para monitorizar la calidad del agua y el proceso de alimentación en piscifactorías y otros entornos. Primero estudiamos en detalle los requisitos de los sensores en acuicultura. Luego mostramos el estado del arte de los sensores actuales para el monitoreo de la calidad del agua y para el monitoreo de la acuicultura. A continuación, presentamos el diseño y desarrollo de nuestros propios sensores de bajo costo y su aplicación en instalaciones de piscifactorías con sistema abierto y sistema de recirculación. Además, mostramos la posibilidad de monitorizar hasta 10 parámetros incluyendo calidad del agua (temperatura, salinidad, turbidez y presencia de hidrocarburo / capa de aceite), ambiente del tanque (nivel de agua, iluminación, presencia de trabajadores) y comportamiento de alimentación de peces (profundidad de natación de bajura, estimación de los cambios en la velocidad de nado de bajíos y la caída de alimento). El sistema propuesto, capaz de monitorear todos estos parámetros, tiene un bajo coste y bajo consumo de energía. El precio estimado es inferior a 100 € por tanque. Además, mostramos el uso de algunos de los sensores antes mencionados para el ajuste automático del proceso de alimentación de peces. Finalmente, mostramos como algunos de los sensores desarrollados se utilizan en otras áreas acuáticas naturales como manglares y estuarios. Además, se presenta un sistema inteligente para monitorear y rastrear la contaminación en los cuerpos de agua.There are many efforts done in the aquaculture to reach its sustainability, although in reality, it is far from being sustainable. Its negative impacts on the environment can be prevented and corrected by the use of sensors, developing precision aquaculture. The water quality affects to the fish performance. The temperature and salinity are some factors that affect to the fish growth. Nevertheless, other factors such as turbidity, photoperiod and dissolved oxygen among other can affect to the fish feeding needs. To adjust the amount of feed needed is crucial to ensure the sustainability of the aquaculture and to increase the economic profit of the facilities. Monitoring the water quality allows estimating the feed needs. However, it is not enough. To monitor the fish behavior, especially during the feeding period can help to adapt the provided feed. Then, if it is so clear that the monitoring can help to the aquaculture production, why we do not see this monitoring systems in the aquaculture facilities? Why in most of the facilities the feed is given manually without considering the fish feeding behavior? Nevertheless, the current price of the sensors for monitoring the fish farms is extremely high. The employed sensors are in most of the cases, the same that are used for oceanography. The proposed systems in the literature only cover some water quality parameters and usually do not consider fish feeding behavior. It is need low-cost sensors suitable for aquaculture monitoring. Those sensors must also be low-energy consumption, easy to use and with the option to include them in a network in order to send the data. Thus, we can use these sensors and sensors network to monitor the activity, to send alarms if it is necessary and to automatize processes. Moreover, including Internet, the data can be seen remotely. The use of those sensors can help to the aquaculture production. In this thesis, we show the study of requirements and design of sensors for monitoring water quality and feeding process in fish farms and other environments. First, we study in detail the requirements of sensors in aquaculture. Then, we show the state of the art of the current sensors for water quality monitoring and for aquaculture monitoring. Following, we present the design and development of some low-cost sensors and their applications in fish farm facilities with open system and recirculating system. Moreover we show a complete system which monitors up to 10 parameters including water quality (temperature, salinity, turbidity and presence of hydrocarbon/oil layer), tank environment (water level, illumination, presence of workers), and fish feeding behavior (shoal swimming depth, estimation of changes on shoal swimming velocity and feed falling). Moreover, it accomplishes the features of low-cost and low energy consumption. The estimated price for proposed system is less than 100€ per tank. In addition, we show the use of some of the aforementioned sensors for automatic adjustment of fish feeding process. Finally, some of the developed sensors are plied in other natural aquatic areas such as mangroves, and estuaries. Moreover, an intelligent system for pollution monitoring and tracking in water bodies are presented.S'estan realitzant molts esforços en l'aqüicultura per assolir la sostenibilitat, malgrat això, encara està lluny de ser sostenible. Els seus impactes sobre el medi ambient es poden prevenir i corregir mitjançant l'ús de sensors, desenvolupant la coneguda com a aqüicultura de precisió. La qualitat de l'aigua afecta el rendiment dels peixos. La temperatura i la salinitat són alguns factors que afecten el creixement dels peixos. A més a més, altres factors com la terbolesa, el fotoperíode i l'oli dissolt entre uns altres poden afectar a les necessitats nutritives dels peixos. Ajustar la quantitat d'aliment necessari és crucial per garantir la sostenibilitat de l'aqüicultura i per augmentar el benefici econòmic de les instal·lacions. Al monitoritzar la qualitat de l'aigua, és possible estimar les necessitats d'alimentació. No obstant això, no és suficient. Monitoritzar el comportament dels peixos, especialment durant el període d'alimentació, pot ajudar a adaptar el subministrament alimentari. Aleshores, si es tan clar que el monitoratge pot ajudar a la producció aqüícola, per què no veiem aquest sistema de monitoratge en les instal·lacions aquàtiques? Per què a la majoria de les instal·lacions la alimentació es dóna manualment sense considerar el comportament alimentari dels peixos? El preu dels sensors per controlar les piscifactories és extremadament alt. Els sensors empleats són, en la majoria dels casos, els mateixos que es fan servir per a l'oceanografia. Els sistemes proposats en la literatura monitoritzen pocs paràmetres de qualitat de l'aigua i generalment no consideren el comportament dels peixos durant l'alimentació. Són necessaris sensors de baix cost adequats per a la monitorització de l'aqüicultura. Aquests sensors han de ser de baix cost, baix consum d'energia, senzills d'usar i amb la possibilitat d'incloure'ls en una xarxa per enviar-los. Per tant, podrem utilitzar aquesta xarxa de sensors i sensors per controlar l'activitat, enviar alarmes si és necessari i automatitzar els processos. A més, si incloem Internet, les dades es podran veure de forma remota. L'ús d'aquests sensors pot ajudar a la producció aqüícola. En aquesta tesi es mostra l'estudi dels requisits i el disseny de sensors per a monitoritzar la qualitat de l'aigua i el procés d'alimentació en piscifactories i altres entorns. Primer, estudiem en detall els requisits dels sensors en aqüicultura. A continuació, es mostra el estat de l'art dels sensors actuals per al monitoratge de la qualitat de l'aigua i per al monitoratge de l'aqüicultura. A continuació, presentem el disseny i desenvolupament dels nostres propis sensors de baix cost i la seva aplicació en instal·lacions d'aqüicultura amb sistema obert i sistema de recirculació. A més, mostrem la possibilitat de monitoritzar fins a 10 paràmetres, incloent-hi la qualitat de l'aigua (temperatura, salinitat, terbolesa i presència d'hidrocarburs / capa d'oli), ambient del tanc (nivell d'aigua, il·luminació, presència de treballadors) i alimentació del consum de peces (profunditat de natació de baix, estimació dels canvis en la velocitat de naixement de baixos i la caiguda d'aliment). El sistema proposat, capaç de controlar tots aquests paràmetres, té un baix cost i baix consum d'energia. El preu estimat és inferior a 100 € per tanc. A més, mostrem l'ús d'alguns dels sensors abans esmentats per a l'ajust automàtic del procés d'alimentació de peces. Finalment, mostrem com alguns dels sensors desenvolupats es fan servir en altres àrees aquàtiques naturals com manglars i estuaris. A més, es presenta un sistema intel·ligent per monitoritzar i rastrejar la contaminació en els cossos d'aigua.Parra Boronat, L. (2018). Study of Requirements and Design of Sensors for Monitoring Water Quality and Feeding Process in Fish Farms and Other Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106369TESI

Design and deployment of a smart system for data gathering in aquaculture tanks using wireless sensor networks

[EN] The design of monitoring systems for marine areas has increased in the last years. One of the many advantages of wireless sensor networks is the quick process in data acquisition. The information from sensors can be processed, stored, and transmitted using protocols efficiently designed to energy saving and establishing the fastest routes. The processing and storing of data can be very useful for taking intelligent decisions for improving the water quality. The monitoring of water exchange in aquaculture tanks is very important to monitor the fish welfare. Thus, this paper presents the design, deployment, and test of a smart data gathering system for monitoring several parameters in aquaculture tanks using a wireless sensor network. The system based on a server is able to request and collect data from several nodes and store them in a database. This information can be postprocessed to take efficient decisions. The paper also presents the design of a conductivity sensor and a level sensor. These sensors are installed in several aquaculture tanks. The system was implemented using Flyport modules. Finally, the data gathering system was tested in terms of consumed bandwidth and the delay Transmission Control Protocol (TCP) packets delivering data from the sensors.This work has been partially supported by the Postdoctoral Scholarship “Contratos Postdoctorales UPV 2014 (PAID‐ 10‐14)” of the “Universitat Politècnica de València,” by the “Programa para la Formación de Personal Investigador— (FPI‐2015‐S2‐884)” of the “Universitat Politècnica de València,” and by the pre‐doctoral student grant “Ayudas para contratos predoctorales de Formación del Profesorado Universitario FPU (Convocatoria 2014)” Reference: FPU14/ 02953 by the “Ministerio de Educación, Cultura y Deporte,” by Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), and Covilhã Delegation, by the National Funding from the FCT—Fundação para a Ciência e a Tecnologia through the UID/EEA/500008/2013 Project, by the Government of Russian Federation, Grant 074‐U01, and by Finep, with resources from Funttel, Grant No. 01.14.0231.00, under the Radiocommunication Reference Center (Centro de Referência em Radiocomunicações —CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicações—Inatel), Brazil.Parra-Boronat, L.; Sendra, S.; Lloret, J.; Rodrigues, JJPC. (2017). Design and deployment of a smart system for data gathering in aquaculture tanks using wireless sensor networks. International Journal of Communication Systems. 30(16):1-15. https://doi.org/10.1002/dac.3335S115301

The Use of Sensors for Monitoring the Feeding Process and Adjusting the Feed Supply Velocity in Fish Farms

[EN] Aquaculture is a growing industry, and its sustainability is crucial. One of its major environmental impacts is the uneaten feed that pollutes the water. To minimize the uneaten feed, many systems have been developed. Nevertheless, current systems can be improved by considering the fish position in the tank and the falling feed. In this paper, we propose a system based on fish presence sensors set at different tank heights and a feed detection sensor located in the drainage tubes. The fish presence sensor is based on light-dependent resistor (LDR). The calibration of these sensors is shown. When the output voltage is higher than 1.467 V, we can consider that fish are present. On the other side, the falling feed sensor is based on a CMOS sensor. The calibration process is performed with 40 pictures. The summation of pixels, with brightness value between 0 and 15 in the blue histogram, is used as an indicator of feed presence. If this value is higher than 520 pixels, we can consider that there is feed in the picture. Moreover, a verification process of both sensors is done. The results of the verification confirm the calibration. Finally, the operation of the system is shown.The authors acknowledged "Ministerio de Educacion, Cultura y Deporte," through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario (FPU) (Convocatoria 2014)" (Grant number FPU14/02953).Parra-Boronat, L.; García-García, L.; Sendra, S.; Lloret, J. (2018). The Use of Sensors for Monitoring the Feeding Process and Adjusting the Feed Supply Velocity in Fish Farms. Journal of Sensors. 2018. doi:10.1155/2018/1060987S2018Jones, A. C., Mead, A., Kaiser, M. J., Austen, M. C. V., Adrian, A. W., Auchterlonie, N. A., … Brown, J. H. (2014). Prioritization of knowledge needs for sustainable aquaculture: a national and global perspective. Fish and Fisheries, 16(4), 668-683. doi:10.1111/faf.12086Parra, L., Sendra, S., García, L., & Lloret, J. (2018). Design and Deployment of Low-Cost Sensors for Monitoring the Water Quality and Fish Behavior in Aquaculture Tanks during the Feeding Process. Sensors, 18(3), 750. doi:10.3390/s18030750Tal, Y., Schreier, H. J., Sowers, K. R., Stubblefield, J. D., Place, A. R., & Zohar, Y. (2009). Environmentally sustainable land-based marine aquaculture. Aquaculture, 286(1-2), 28-35. doi:10.1016/j.aquaculture.2008.08.043Qi, L., Zhang, J., Xu, M., Fu, Z., Chen, W., & Zhang, X. (2011). Developing WSN-based traceability system for recirculation aquaculture. Mathematical and Computer Modelling, 53(11-12), 2162-2172. doi:10.1016/j.mcm.2010.08.023Primavera, J. H. (2006). Overcoming the impacts of aquaculture on the coastal zone. Ocean & Coastal Management, 49(9-10), 531-545. doi:10.1016/j.ocecoaman.2006.06.018Camargo, J. A., & Alonso, Á. (2006). Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystems: A global assessment. Environment International, 32(6), 831-849. doi:10.1016/j.envint.2006.05.002Papadakis, V. M., Papadakis, I. E., Lamprianidou, F., Glaropoulos, A., & Kentouri, M. (2012). A computer-vision system and methodology for the analysis of fish behavior. Aquacultural Engineering, 46, 53-59. doi:10.1016/j.aquaeng.2011.11.002Saberioon, M., Gholizadeh, A., Cisar, P., Pautsina, A., & Urban, J. (2016). Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Reviews in Aquaculture, 9(4), 369-387. doi:10.1111/raq.12143Armstrong, J. D., Braithwaite, V. A., & Rycroft, P. (1996). A flat-bed passive integrated transponder antenna array for monitoring behaviour of Atlantic salmon parr and other fish. Journal of Fish Biology, 48(3), 539-541. doi:10.1111/j.1095-8649.1996.tb01446.xConti, S. G., Roux, P., Fauvel, C., Maurer, B. D., & Demer, D. A. (2006). Acoustical monitoring of fish density, behavior, and growth rate in a tank. Aquaculture, 251(2-4), 314-323. doi:10.1016/j.aquaculture.2005.06.018Zhang, H., Wei, Q., & Kang, M. (2014). Measurement of swimming pattern and body length of cultured Chinese sturgeon by use of imaging sonar. Aquaculture, 434, 184-187. doi:10.1016/j.aquaculture.2014.08.024Atoum, Y., Srivastava, S., & Xiaoming Liu. (2015). Automatic Feeding Control for Dense Aquaculture Fish Tanks. IEEE Signal Processing Letters, 22(8), 1089-1093. doi:10.1109/lsp.2014.2385794Bórquez-Lopez, R. A., Casillas-Hernandez, R., Lopez-Elias, J. A., Barraza-Guardado, R. H., & Martinez-Cordova, L. R. (2018). Improving feeding strategies for shrimp farming using fuzzy logic, based on water quality parameters. Aquacultural Engineering, 81, 38-45. doi:10.1016/j.aquaeng.2018.01.002Papandroulakis, N., Dimitris, P., & Pascal, D. (2002). An automated feeding system for intensive hatcheries. Aquacultural Engineering, 26(1), 13-26. doi:10.1016/s0144-8609(01)00091-7Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654Covès, D., Beauchaud, M., Attia, J., Dutto, G., Bouchut, C., & Bégout, M. L. (2006). Long-term monitoring of individual fish triggering activity on a self-feeding system: An example using European sea bass (Dicentrarchus labrax). Aquaculture, 253(1-4), 385-392. doi:10.1016/j.aquaculture.2005.08.015Zhou, C., Lin, K., Xu, D., Chen, L., Guo, Q., Sun, C., & Yang, X. (2018). Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture. Computers and Electronics in Agriculture, 146, 114-124. doi:10.1016/j.compag.2018.02.006Zhou, C., Zhang, B., Lin, K., Xu, D., Chen, C., Yang, X., & Sun, C. (2017). Near-infrared imaging to quantify the feeding behavior of fish in aquaculture. Computers and Electronics in Agriculture, 135, 233-241. doi:10.1016/j.compag.2017.02.013Parra, L., Rocher, J., Escrivá, J., & Lloret, J. (2018). Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms. Aquacultural Engineering, 81, 10-18. doi:10.1016/j.aquaeng.2018.01.004Sendra, S., Llario, F., Parra, L., & Lloret, J. (2014). Smart Wireless Sensor Network to Detect and Protect Sheep and Goats to Wolf Attacks. Recent Advances in Communications and Networking Technology, 2(2), 91-101. doi:10.2174/2211740711201666001

An internet of things framework for real-time aquatic environment monitoring using an Arduino and sensors

Aquaculture is the farming of aquatic organisms in natural, controlled marine and freshwater environments. The real-time monitoring of aquatic environmental parameters is very important in fish farming. Internet of things (IoT) can play a vital role in the real-time monitoring. This paper presents an IoT framework for the efficient monitoring and effective control of different aquatic environmental parameters related to the water. The proposed system is implemented as an embedded system using sensors and an Arduino. Different sensors including pH, temperature, and turbidity, ultrasonic are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a comma-separated values (CSV) file in an IoT cloud named ThingSpeak through the Arduino microcontroller. To validate the experiment, we collected data from 5 ponds of various sizes and environments. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), temperature (16-24 °C), turbidity (below 10 ntu), conductivity (970-1825 μS/cm), and depth (1-4) meter. At the end of this paper, a complete hardware implementation of this proposed IoT framework for a real-time aquatic environment monitoring system is presented

Design and Implementation of Wireless Sensors and Android Based Application for Highly Efficient Aquaculture Management System

The main problems in the practice of traditional shrimp aquaculture are related with maintaining good water quality and reducing high operational cost. In this paper it will be described the application of wireless sensors and Android based application as mobile monitoring tool in achieving highly efficient shrimp aquaculture monitoring system. A set of four water quality parameter sensors (pH, temperature, conductivity and DO) were submerged into the pond using a buoy, in which an electronics and Xbee wireless transmitter have been installed to transmit the measured data into a fixed monitoring station. The main component of the fixed monitoring station was a smart data logger capable of performing automatic aeration system. Data transmission from the monitoring station to the master station was done through GSM/GPRS module of a Raspberry microcontroller. Using internet connection, a web based server has been developed from which the Android based application retrieved the measured parameter data. Graphical analysis of water quality data can be performed from a mobile phone, allowing users to monitor the aquaculture regardless of their geographical location. This system has been implemented in a shrimp aquaculture in Bangka island, Indonesia. In addition to giving real-time water quality data, the system was able to reduce the operational electricity cost because of the automatic aeration feature. Consistenly, the system has been sending the measurement data to the web server, which is accessible using Android mobile phones worldwide

Physical Sensors for Precision Aquaculture: A Review

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this[EN] Aquaculture is presented as a sustainable method to provide fish, although in reality, it is far from being sustainable. Its negative impacts on the environment can be prevented and corrected by the use of sensors, developing precision aquaculture. Sensors are widely used in terrestrial applications, but in underwater environments, their use is constrained by a variety of issues. The aim of this paper is to describe the state-of-the-art of the underwater sensors for water quality monitoring. First, the requirements and challenges of underwater sensors for aquaculture monitoring are discussed in detail. The main challenges are the need of a waterproof isolation or the need to avoid corrosion and biofouling, among others. Second, there are some advantages compared with the terrestrial applications, such as no need of minimized systems or the fact that such systems only require low accuracy. Subsequently, we evaluated the different options available to sense each variable, related to the needs of the aquaculture sensors. For temperature monitoring, thermistors, thermocouples or RTC seem to offer similar advantages. In contrast, for dissolved oxygen monitoring, the optical method seems to be the best option. For turbidity, optical methods are the most employed ones, while for conductivity measurements, the inductive coils are a promising option.This work was supported by the pre-doctoral student grant "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)" with reference: FPU14/02953 by the Ministerio de Educacion, Cultura y Deporte.Parra-Boronat, L.; Lloret Mauri, G.; Lloret, J.; Rodilla, M. (2018). Physical Sensors for Precision Aquaculture: A Review. IEEE Sensors Journal. 18(10):3915-3923. https://doi.org/10.1109/JSEN.2018.2817158S39153923181

Water quality monitoring in recirculating aquaculture systems

Good water quality in recirculating aquaculture systems (RAS) is crucial for ensuring the successful growth and survival of reared species. So far, there are no regulations for which parameters should be measured in RAS, and each farmer decides which parameters to follow. Traditionally, water quality parameters have been measured at certain intervals with handheld sensors and laboratory analyses, which can be labour intensive. Currently, a variety of sensors and monitoring equipment is available, even for the real-time monitoring of water quality parameters. Internet of Things-based systems and artificial intelligence can be applied for the monitoring purposes which allows real-time measurements and warnings of critical situations. However, many of the modern systems need competent users and require regular maintenance and calibration. Changes in water quality also induces changes in fish behaviour, such as swimming activity, depth, acceleration and water quality can be assessed also based on these changes. In this review, water quality parameters, variety of sensors and monitoring technologies have been summarised to provide an overview of the current monitoring systems for water quality. Additionally, analytical methods for more advanced analyses have also been briefly summarised. Although there are several advanced options available for monitoring the basic water quality parameters, real-time measurements of more advanced parameters still required require further development

Increasing Sustainable Bivalve Aquaculture Productivity Using Remote Non-Invasive Sensing and Upweller Technologies

The work and findings described by this thesis aim to develop technologies and approaches relevant to bivalve aquaculture, focusing on non invasive sensing to monitor bivalve shellfish, primarily the Pacific oyster (Magallana gigas). Following the introduction, Chapter 2 presents an overview of the Non Invasive Oyster Sensor (NOSy), a sensor developed at the University of Essex that records bivalve openness (gape). NOSy was conceived to automatically detect spawning as an aid to oyster growers and has proved useful in field and laboratory, work which underpins three chapters in this thesis. NOSy remains under development, and has potential for use in aquaculture, monitoring and research. Chapter 3 assesses the role of salinity in driving estuarine oyster behaviour. We replicated an estuarine tidal salinity cycle and recorded the gape of oysters exposed to it. Behaviours during the experiment did not resemble those in the estuary, suggesting that salinity alone does not drive estuarine oyster behaviour. We also discuss the challenges of controlling salinity in a laboratory, and suggest it is an under-studied area. Chapter 4 discusses land based systems for young oyster growing. Land-based systems have the potential to improve growth, condition and survival while reducing labour and maintenance costs. We trialled a system over three summers, with promising results. Reduction of localised densities improved growth rate and uniformity. Cost forecasts suggest that adoption of land based growing systems could result in substantial savings. Chapter 5 presents gaping records from an area where Blue mussels (Mytilus edulis) have become non harvestable in recent years due to contamination. We used NOSy to assess gaping patterns of the mussel population to evaluate how their behaviours affect their vulnerability to contamination. Mussels in the bay closed over low tide as a response to extremely low salinity, inferring protection from contamination by limiting the mussel’s exposure

New fish product ideas generated by European consumers

Food lifestyles are changing; people have less time to spend on food purchase and preparation, therefore leading to increasing demand for new food products. However, around 76% of new food products launched in the market fail within the first year (Nielsen, 2014). One of the most effective ways to enhance new products’ success in the market is by incorporating consumers’ opinions and needs during the New Product Development (NPD) process (Moon et al., 2018). This study aimed to explore the usefulness of a qualitative technique, focus groups, to generate new aquaculture fish product ideas as well as to identify the most relevant product dimensions affecting consumers’ potential acceptance.Peer ReviewedPostprint (published version