The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

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

Integration of IoT and chatbot for aquaculture with natural language processing

The development of internet of things (IoT) technology is very fast lately. One sector that can be implemented by IoT technology is the aquaculture sector. One important factor in the success of aquaculture is a good and controlled water quality condition. But the problem for the traditional aquaculture farmers is to monitor and increase the water quality quickly and efficiently. To resolve the above-mentioned problem, this paper proposes a real-time monitoring system for aquaculture and supported with chatbot assistant to facilitate the user. This system was composed of IoT system, cloud system, and chatbot system. The proposed system consists of 7 main modules: smart sensors, smart aeration system, local network system, cloud computing system, client visualization data, chatbot system, and solar powered system. The smart aeration system consists of NodeMCU, relay, and aerator. The smart sensors consist of several sensors such as dissolved oxygen, pH, temperature, and water level sensor. Natural language processing is implemented to build the chatbot system. By combining text mining processing with naive Bayes algorithm, the result shows the very good performance with high precision and recall for each class to monitor the quality of water in aquaculture sector

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

A Reliable and Efficient Wireless Sensor Network System for Water Quality Monitoring

Wireless sensor networks (WSNs) are strongly useful to monitor physical and environmental conditions to provide realtime information for improving environment quality. However, deploying a WSN in a physical environment faces several critical challenges such as high energy consumption, and data loss.In this work, we have proposed a reliable and efficient environmental monitoring system in ponds using wireless sensor network and cellular communication technologies. We have designed a hardware and software ecosystem that can limit the data loss yet save the energy consumption of nodes. A lightweight protocol acknowledges data transmission among the nodes. Data are transmitted to the cloud using a cellular protocol to reduce power consumption. Information in the cloud is mining so that realtime warning notifications can be sent to users. If the values are reaching the threshold, the server will send an alarm signal to the pond\u27s owner phone, enable him to take corrective actions in a timely manner. Besides, the client application system also provides the feature to help the user to manage the trend of a physical environment such as shrimp ponds by viewing charts of the collected data by hours, days, months. We have deployed our system using IEEE 802.15.4 Standard, ZigBEE, KIT CC2530 of Texas Instrument, and tested our system with temperature and pH level sensors. Our experimental results demonstrated that the proposed system have a low rate of data loss and long energy life with low cost while it can provide real-time data for water quality monitoring

Online Monitoring Kualitas Air Pada Budidaya Udang Berbasis WSN Dan IoT

Dalam tulisan ini dijelaskan desain dan pengembangan sistem online monitoring kualitas air berbasis wireless sensor Network (WSN) dan Internet of Things (IoT). Sistem ini didesain dan dikembangkan untuk memantau parameter DO (Dissolved Oxygen), pH, conductivity dan temperatur pada budidaya udang. Sistem terdiri dari beberapa node sensor dengan komponen utama arduino uno yang terhubung dengan Xbee board dan master board dengan komponen utamanya adalah Raspberry Pi 2 (RPi2) board dan Xbee. Data dikirim dari masing-masing node ke RPi2 menggunakan jaringan WSN dengan paket data yang dilengkapi dengan masing-masing ID, setelah itu data disimpan di database internal RPi2 dan ditampilkan di graph. Timer update server digunakan untuk update data dari RPi2 ke server menggunakan jaringan internet melalui wifi. Data di server dapat dilihat menggunakan website, selain itu juga data dapat dilihat pada aplikasi Telegram Messenger yang ter-install di perangkat ponsel. Program RPi2 dikembangkan menggunakan bahasa python dan komponen matplotlib. Hasil percobaan menunjukkan bahwa sistem memiliki prospek yang besar dan dapat digunakan untuk keperluan budidaya udang dengan memberikan informasi yang relevan dan tepat waktu. Data hasil pengumpulan tersebut dapat digunakan untuk penelitian dan analisa lebih lanjut