The term "smart agriculture" refers to a management concept that is centered on industrial agriculture. It makes use of cutting-edge technologies such as big data, cloud, and the Internet of Things to monitor, automate, and evaluate agricultural operations. Smart agriculture is a management concept. Software and sensors are used in smart agriculture, also known as precision agriculture. Smart agriculture is managed by the software. This pa-per proposes the development of low-cost environment parameters and electrical quantities monitoring device. According to the findings, such Internet of Things devices are suited for digitizing the relevant data in order to ac-quire information that may help farmers make optimal changes.

In agriculture, soil is a vital element that decides the quality and yield of agricultural produce. Soil consists of various nutrients such as nitrogen (N), phosphorous (P), potassium (K), the potential of hydrogen (pH), and water content. Nitrogen is responsible for building chlorophyll, which helps produce proteins and thus directly contributes to plant growth and development. Phosphorous is needed to develop root systems and flowers, whereas potassium helps increase disease resistance. Each of these play a role in crop cultivation. Thus, in this research paper, considering the fact that soil health will provide farmers with the best selection of crops that are compatible with their farm’s soil nutrients, we propose an algorithm for recommending a set of suitable crops based on various soil attributes. These soil nutrients can be collected in real-time using soil sensors, such as N, P, K, and pH, and humidity sensors. They can be deployed in farms where the cultivation takes place. These sensor readings would then be transferred to the blockchain layer, thereby validating the data and ensuring it is tamper-proof and evident. The crop recommendation model uses data from these sensors in real-time, increasing the results’ accuracy. The last stage leads us to display these results via a user dashboard, which helps the farmers to keep in check with their farm’s practices, and their sensor states from remote locations.

The integration of Internet of Things (IoT) technologies in agriculture has emerged as a transformative force, revolutionizing traditional farming practices and driving efficiency and sustainability. This paper presents the development and implementation of a cost-effective greenhouse monitoring system utilizing LoRaWAN technology for data communication. The system’s design, deployment, and performance are discussed in detail. Key components include an array of sensors for monitoring environmental parameters and LoRaWAN for long-range, low-power communication. The low-cost nature of the system challenges the notion that advanced agricultural technology is prohibitively expensive, making it accessible to farmers of varying scales. The system’s affordability and realtime data accessibility make it a valuable tool for precision agriculture, contributing to improved crop yields and resource management.

The transmission network represents undoubtedly the infrastructure that enables the power market. Nowadays operating conditions of the power systems, open access to the transmission network, leading to different transactions within a certain power system or cross-border transactions, are increasing the risk of congestions. Taking into consideration these facts, new tools are necessary to be developed, according to the actual conditions. This paper aims to elaborate an original mathematical model used for congestion management in case of deregulated environment. In the following, a software tool is developed in Matlab environment, designed for probabilistic congestion management. The West and South-West power system is used as a case study.

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity—industrial or commercial—and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted.