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 This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions.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

IoT-based intelligent irrigation management and monitoring system using arduino

Plants, flowers and crops are living things around us that makes our earth more productive and beautiful. In order to growth healthy, they need water, light and nutrition from the soil in order to effect cleaning air naturally and produce oxygen to the world. Therefore, a technology that manage to brilliantly control plants watering rate according to its soil moisture and user requirement is proposed in this paper. The developed system included an Internet of Things (IoT) in Wireless Sensor Network (WSN) environment where it manages and monitors the irrigation system either manually or automatically, depending on the user requirement. This proposed system applied Arduino technology and NRF24L01 as the microprocessor and transceiver for the communication channel, respectively. Smart agriculture and smart lifestyle can be developed by implementing this technology for the future work. It will save the budget for hiring employees and prevent from water wastage in daily necessities

IoT and Neural Network-Based Water Pumping Control System For Smart Irrigation

This article aims at saving the wasted water in the process of irrigation using the Internet of Things (IoT) based on a set of sensors and Multi-Layer Perceptron (MLP) neural network. The developed system handles the sensor data using the Arduino board to control the water pump automatically. The sensors measure the environmental factors; namely temperature, humidity, and soil moisture to estimate the required time for the operation of water irrigation. The water pump control system consists of software and hardware tools such as Arduino Remote XY interface and electronic sensors in the framework of IoT technology. The machine learning algorithm such as the MLP neural network plays an important role to support the decision of automatic control of IoT-based irrigation system, managing the water consumption effectively.Comment: 6 pages, 5 figures, 1 tabl

Precision Agriculture for Water Management Using IOT

In the territory of agriculture, proper use of irrigation is important and it is well known that irrigation by drip approach is very cost effective and efficient.Role of agriculture in the development of agricultural country is very important. The freshly come up wireless sensor network (WSN) technology has growing rapidly into distinct multi-disciplinary fields. Agriculture and farming is one of the management which have freshly switch their consideration to WSN, curious this cost adequate technology to improve its production and boost agriculture yield definitive. The outlook of this paper is to design and develop an agricultural monitoring system using wireless sensor network and IOT to enlarge the productivity and quality of farming without penetrating it for all the time manually. Temperature, humidity and water levels are the most important circumstances for the productivity, growth, and quality of plants in agriculture. The temperature, humidity and water level sensors are set up to cluster the temperature and humidity values. One of the most stimulating fields having an exotic need of decision support systems is Precision Agriculture (PA). Through sensor networks, agriculture can be associated to the IoT, with the help of this approach which provides real-time information about the lands and crops that will help farmers make right decisions. The primary influence is implementation of WSN in Precision Agriculture (PA) with the help of IoT which will enhance the usage of water, fertilizers while expand the yield of the crops and also notifications are sent to farmers mobile periodically. The farmers can able to monitor the field conditions from anywhere

Implementation of IoT and Machine Learning for Smart Farming Monitoring System

In a country like mine, Ethiopia where there are 94 million people, agriculture is the main driver of the country’s economy contributing to 45% GDP and 85% of the population are farmers, Traditional methods used by farmers aren't suf?cient enough to serve the increasing demand and so they have to hamper the soil by using harmful pesticides in an intensi?ed manner. As to increase farm productivity by understanding and forecasting crop performance in a variety of environmental conditions this paper focuses on emerging different automation practices like IoT, Wireless Communications, Machine learning and Arti?cial Intelligence, Deep learning as part of the industry’s technological evolution

State of the Industry 4.0 in the Andalusian food sector

The food industry is a key issue in the economic structure of Andalusia, due to both the weight and position of this industry in the economy and its advantages and potentials. The term Industry 4.0 carries many meanings. It seeks to describe the intelligent factory, with all the processes interconnected by Internet of things (IOT). Early advances in this field have involved the incorporation of greater flexibility and individualization of the manufacturing processes. The implementation of the framework proposed by Industry 4.0. is a need for the industry in general, and for Andalusian food industry in particular, and should be seen as a great opportunity of progress for the sector. It is expected that, along with others, the food and beverage industry will be pioneer in the adoption of flexible and individualized manufacturing processes. This work constitutes the state of the art, through bibliographic review, of the application of the proposed paradigm by the Industry 4.0. to the food industry.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched.This research was supported by Industrial Computers and Computer Networks program (I2RC) (2015/2016) funded by the University of Alicante

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

The Internet of Things (IoT) has achieved an upset in a considerable lot of the circles of our current lives, like automobile, medical services offices, home automation, retail, ed-ucation, manufacturing, and many more. The Agriculture and Farming ventures signifi-cantly affect the acquaintance of the IoT with the world. Machine learning (ML) is a part of artificial intelligence (AI) that permits software applications to turn out to be more precise at foreseeing results without being expressly customized to do as such. It uses historical data as input to predict new result values. In the event, a specific industry has sufficient recorded information to help the machine "learn", AI or ML can create out-standing outcomes. Farming is likewise one such important industry profiting and ad-vancing from machine learning at large. ML can possibly add to the total lifecycle of farming, at all phases. This incorporates computer vision, automated irrigation, and harvesting, predicting the soil, weather, temperature, moisture values, and robots for picking off the crude harvest. In this paper, I'll work on a smart agricultural information monitoring framework that gathers the necessary information from the IoT sensors set in the field, measures it, and drives it, from where it streams to store in the cloud space. The information is then shipped off the prediction module where the necessary analysis is done using ML algorithms and afterward sent to the UI for its corresponding applica-tion

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201