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 This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions.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 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

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

Design of Turbine Aerator with Remote Control and Internet of Things (IoT)-Based Water pH Monitoring

Water plays a very important role for living things including fish, with good water fish can grow optimally and healthily. The acidic and alkaline content of water and also oxygen greatly affects its growth. Currently, the majority of fish farmers monitor the pH and oxidation process of the pond manually. Therefore, in an aquaculture business, water quality must be monitored by fish farmers. In this research, an internet of things (IoT) based tool will be made that will produce oxygen in the water in tilapia ponds and is equipped with a pH sensor that will read how much pH value is contained in it, then the data can be viewed remotely via a cellphone connected to the internet. The telemetry system of this aerator research uses the NodeMCU ESP8266 microcontroller then the pH sensor reading data can be seen through the cellphone with the Blynk application as well as the aerator control can be easily done from the application. fish farmers can easily monitor the quality of water pH in real-time as well as control the aerator. The results achieved by the aerator can cause the oxidation process (dissolved oxygen) in water from the rotation of the impeller. Testing was carried out on a tilapia pond with a pond diameter of 15m2. The methodology used is quantitative with the results obtained from 10x experiments and comparison of the pH sensor and also the pH meter shows 96% accuracy of the pH sensor 4502C while 4% for the error value. the pH value before the aerator is active is 6 which means acidic. After the aerator is active and the dissolved oxygen process runs the pH value of the water becomes 7-7.5 which means neutral, this value is good for freshwater fish to breed well. from the help of this tool, fish farming farmers can more efficiently monitor water pH and aerator control

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