Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

In recent decades, climate change and a shortage of resources have brought about the need for technology in agriculture. Farmers have been forced to use information and innovation in communication in order to enhance production efficiency and crop resilience. Systems engineering and information infrastructure based on the Internet of Things (IoT) are the main novel approaches that have generated growing interest. In agriculture, IoT solutions according to the challenges for Industry 4.0 can be applied to greenhouses. Greenhouses are protected environments in which best plant growth can be achieved. IoT for smart greenhouses relates to sensors, devices, and information and communication infrastructure for real-time monitoring and data collection and processing, in order to efficiently control indoor parameters such as exposure to light, ventilation, humidity, temperature, and carbon dioxide level. This paper presents the current state of the art in the IoT-based applications to smart greenhouses, underlining benefits and opportunities of this technology in the agriculture environment.

Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use.

Driving while fatigued is dangerous and may result in car accidents. Moreover, adding technology to assist driving can distract the driver. In this work, we present a noninvasive and non-distracting method for monitoring driver emotions and fatigue. To the best of our knowledge, no scientifically agreed definition of emotions exists, as a physical state to be monitored. Emotion recognition is defined as measuring observations of motor system behavior that correspond with high probability to an underlying emotion or combination of emotions. This definition is based on the fact that measuring cognitive influences is currently impossible. In this work we have investigated driving sessions for identifying possible risks at drive in a certain itinerary, analyzing HRV trend. In general, HRV signals can be used as indicators of the responses of the autonomic nervous system (ANS) because the ANS is influenced by the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The first one (SNS) response to an alarm situation: struggle, stress, drowsiness (and other factors) while PNS, when is activated, produces a slowing down of heart rate and breathing therefore PNS is the normal response to a situation of calmness and absence of danger and stress. For the test, we have collected ECG signals (Electrocardiography) and driver position in 15 trips on 2 daily sessions, by 2 different drivers on the same path. Results show an interesting geospatial relationship of emotions on the travelled path.

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems’ interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure.

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one.