In an era characterized by severe climate change, dwindling resources, and a growing world population, the agricultural industry is facing unprecedented challenges. On the other hand, overuse of natural resources has emerged as a major concern worldwide. Greenhouses (GHs) have been developed as central environments capable of growing a diverse range of high-quality agricultural products throughout the year, regardless of external weather conditions. However, conventional GHs often impose significant costs on energy resources for their heating and cooling operations, thus presenting sustainability challenges. To address these pressing concerns, using new smart technologies as well as the integration and development of renewable energy sources, including photovoltaics (PVs), wind turbines (WT), and geothermal systems, have gained momentum. This integration not only increases the ecological footprint of GHs but also reduces their dependence on conventional energy sources. Furthermore, the adoption of smart GH technologies, characterized by advanced control and automation systems, holds significant promise in energy optimization and efficiency. Hence, this systematic review attempts to carefully examine the optimal and practical methods that include the design, fabrication, control, energy management, and operation of smart GHs. This review includes an in-depth analysis of GH structures, building materials, cooling and heating systems, new dark GH concepts, and smart lighting systems. In addition, it addresses effective strategies to curb energy consumption in smart GHs. By synthesizing and synthesizing existing research and practical experiences, this paper seeks to provide valuable insights and recommendations to facilitate the efficient and sustainable design, construction, and operation of smart GHs. Ultimately, this work aims to promote resource-efficient and environmentally conscious practices in the agricultural sector.

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.