• Rural Digital Europe
• 2013-2022close
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The purpose of this paper is to address current issues in industrial robotics applications and to address also the current trends occurring in the industry regarding smaller mobile units in factory and external manufacturing processes relating to the battery powering of robotics devices. Specifically, we shall concentrate the efforts herein on “Mobile Industrial Robotics”. The paper touches briefly on problems associated with both definitions of industrial robots and problems associated with power supply sources of mobile industrial robotics and provides an outline of future work with appropriate solution for smaller mobile industrial mobile units operating in the variable input range of 24V to 48v and outputs in the range of 3.3V to 12V @ 20A in three stages. The project required a fast, turn-key solution which did not allow for a “design from scratch” solution and so detailed herein is suitable design produced in other sectors and relating the details, design process, modelling and examination of a successful and efficient high current DC to DC switch mode power supply. Parameters have been adjusted to our own preferences in the design considerations regarding “Industrial Service Hybrids” [1] (ISH)1. Other design issues are under assessment and shall be included as the project moves forward.

Both agricultural and environmental domains have to manage many different and heterogeneous sources of information that need to be combined in order to make environmentally and economically sound decisions. Such examples may be found at the definition of subsidies, national strategies for rural development, development of sustainable agriculture etc. This paper describes in detail the development of an open data model for (precision) agriculture applications and agricultural pollution monitoring when aiming at identification of requirements from users from agricultural and environmental domains. The presented open data model for (precision) agriculture applications and agricultural pollution monitoring has been registered under the GEOSS (Global Earth Observation System of Systems) Architecture Implementation Pilot – Phase 8 in order to support the wide variety of demands that are primary aimed at agriculture and water pollution monitoring.

Highly productive dairy cow' ration under intensive production conditions causes the development of subacute rumen acidosis (SARA). In this paper we discuss a reticulo-ruminal long-acting cyber-physical diagnostic system' prototype, which applies Internet of Things (IoT) for monitoring rumen parameters of cows. The new diagnostic system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes gateways for data collection from boluses, an MQTT (Message Queuing Telemetry Transport) broker, a web server and a database. The diagnostic system' prototype provides timely data on cow health status to the users, so they can evaluate the cow' health status and implement further actions. Therefore, the use of diagnostic system provides opportunity to increase cow productivity, longevity and to save maintenance cost of milk farms. The results of the first stage of the research are discussed in this paper.

In this paper we report a power monitoring system used in the industrial greenhouse. Monitored data is processed in the device and then transferred to secure cloud services, where the data can be further processed, formatted and sent to the server used for data storage and later analysis. The power monitoring system is a part of the project where various parameters are measured in the greenhouse and as a result, could offer guidelines for the more efficient growth of plants [1] [2].

Remote sensing has proved to be an accurate and reliable tool in clear water environments like oceans or the Mediterranean Sea. However, the current algorithms and methods usually fail on optically complex waters like coastal and inland waters. The whole Baltic Sea can be considered as optically complex coastal waters. Remote assessment of water quality parameters (eg., chlorophyll-a concentration) is of interest for monitoring of marine environment, but hasn’t been used as a routine approach in Latvia. In this study, two simultaneous hyperspectral airborne data and in situ measurement campaigns were performed in the Gulf of Riga near the River Daugava mouth in summer 2015 to simulate Sentinel-3 data and test existing algorithms for retrieval of Level 2 Water products. Comparison of historical data showed poor overall correlation between in situ measurements and MERIS chlorophyll-a data products. Better correlation between spectral chl-a data products and in situ water sampling measurements was achieved during simultaneous airborne and field campaign resulting in R2 up to 0.94 for field spectral data, R2 of 0.78 for airborne data. Test of all two band ratio combinations showed that R2 could be improved from 0.63 to 0.94 for hyperspectral airborne data choosing 712 and 728 nm bands instead of 709 and 666 nm, and R2 could be improved from 0.61 to 0.83 for simulated Sentinel-3 OLCI data choosing Oa10 and Oa8 bands instead of Oa11 and Oa8. Repeated campaigns are planned during spring and summer blooms 2016 in the Gulf of Riga to get larger data set for validation and evaluate repeatability. The main challenges remain to acquire as good data as possible within rapidly changing environment and often cloudy weather conditions.

The modern autonomous beekeeping system developed in this research is the real example of Internet of Things technologies (IoT) in the beekeeping sector. It performs a bee colony maintenance control without interfering with its processes, while optimizing frequency of the apiary inspection. The system helps to analyze data correlation with video, meteo data, mass changes in time as well as interpretation of nest temperature, humidity and linking to local geographic and biological conditions. It allows a beekeeper to request and receive key data indicators and in accordance with the indicators to react on time and provide the best required maintenance of the bee colony. By implementing the autonomous beekeeping, the hives conditions can be tracked remotely, e.g. whether the inside temperature is critical, if the family is missing feed, therefore the critical deviation can be detected and prevented in time.

Nowadays industrial greenhouses are implementing various Internet of Things (IoT) technologies, allowing to easily integrate sensor technologies with wireless communications and create cloud-based database solutions. For industrial greenhouses, one of an important parameter is irrigation of crop and also crop biomass growth during the day. Paper deals with the development of IoT based weight sensor for continuous weight measurements in an industrial greenhouse environment, practical data analysis, and new algorithm development for measurement precision improvement. Real measurements are described and results of two algorithm application are compared.