In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.

The placement of grazing animals in vineyards requires additional support to the animal husbandry activities. Such support must include the monitoring and the conditioning of animal's location and behavior, specially their feeding posture. With such a system, it is possible to allow sheep to graze in cultivated areas (e.g. vineyards, orchards) without endangering them. This paper proposes an animal behavior monitoring platform, based on IoT technologies. It includes an IoT local network to gather data from animals and a cloud platform, with processing and storage capabilities, to autonomously shepherd ovine within vineyard areas. The cloud platform also incorporates machine learning features, allowing the extraction of relevant information from the data gathered by the IoT network. Thus, besides the platform description, some results are presented regarding the machine learning platform. Namely, this platform was evaluated for detecting and defining conditions respecting animal's posture, with preliminary promising results. Since several algorithms were tested, this paper includes a comparison of those algorithms.

Intelligent farming is one of the vast range of applications covered by the Internet of Things concept. Notwithstanding, such applications present specific requirements and constraints that are dependent on their purpose. A practical case on which that is particularly relevant is the SheepIT project, where an automated IoT-based system controls grazing sheep within vineyards, guaranteeing that they do not threaten cultures. Due to its rigid requirements, particularly regarding the deployment of the Wireless Sensor Network, Machine-2-Machine communications and necessary interactions with a computational platform available through the Internet, Internet Protocol-based solutions are not suitable. Consequently, a customized communication stack has been developed, that intends to meet the project requirements, from the physical to the Application Layers. Although it has been developed considering the SheepIT requirements, its use may be extended to more generic intelligent farming applications, since most of the requirements are directly related with the farming environment. This paper reviews the proposed stack and details the recent developments. Particularly, we focused on Internet of Things/Machine-2-Machine interaction, describing the design and deployment of a gateway that addresses the SheepIT service requirements. Additionally, and complementary to previously published results, we evaluate the gateway performance and show its feasibility and scalability in a real scenario.

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.