# GreenLab, an IoT-Based Small-Scale Smart Greenhouse
## 2 Related Work  
With the appearance of the IoT concept, new smart sensing, controlling, and communication technologies have been immediately integrated into smart farming solutions. An interesting smart greenhouse model at a small scale is presented in [10]. During the study, the dependence of plant growth on the parameters of the micro-climate was determined. Plants are living organisms, for which, when building greenhouses, we must take into account the maintenance of their health and adaptability to external conditions. Optimizing the conditions for growing vegetable crops in the greenhouse's soil depends on the level of technical equipment that ensures operations such as heating, lighting, watering, feeding, and plant care [10]. Smart greenhouse environmental control systems using sensor networks are becoming more widespread and sophisticated. These systems collect data about the environment in which the plants are grown and how they grow in order to obtain the highest possible productivity and the best possible quality of vegetables and fruits. It is difficult for system users without farming experience to properly configure and control the parameters of multiple devices. In [11], an intelligent greenhouse environmental control system based on the algorithm \"sliding window-based support vector regression\" (SW-SVR) is described. The proposed system performs prediction control based on accurate predictions in real-time. SW-SVR is a new machine learning algorithm for time series data prediction. The automatic prediction model periodically adapts to the current environment, anticipating the evolution of data over time with high accuracy and low computational complexity [11]. Using cloud services for agriculture reduces the burden of numerous devices to be installed in environments with high temperatures and increased humidity. Thus, the usage of integrated predictive analytics as a cloud service is an alternative for reducing the number of sensors while also providing parameter analysis and forecasting services [12]. Besides parameter monitoring and control, the use of Wireless Power Transmission (WPT) can solve a thorny problem in smart greenhouses, i.e., powering sensors located in different parts of the greenhouse, often hard to reach for classic wiring through wires for power supply. An interesting method is presented in [13]. The monitoring area in a smart greenhouse is large, and a large number of sensor nodes are needed to form a complete and relevant monitoring network. Information such as temperature inside the greenhouse, air humidity, soil moisture, and also lighting can be collected through dedicated sensors for an automatic monitoring of all relevant parameters. In this context, the energy charging of sensors via wireless using mobile wireless charging devices is important for a smart greenhouse. This implicitly also leads to a reduction in the initial costs of the smart greenhouse and a reduction in the human resources specialized in the maintenance of the power systems. Another interesting model is presented in [14]. To solve the problem of sensor charging, the authors propose the \"enclose-loop\" sensor using wireless power technology. The energy transfer efficiency for the magnetic resonance coupling method and its relationship with the transferred energy power are analyzed. Another paper proposes an innovative use of the uniform magnetic field in a large space to charge the sensors in the intelligent greenhouse wirelessly, leading to the avoidance of the disadvantages of cable charging [15]. A smart greenhouse needs a lot of energy to carry out all technological processes. A solution to reduce energy consumption from the grid is to install photovoltaic panels with additional capabilities to track the maximum power of the sun's rays so that as much solar energy as possible is taken for the operation of the mini-greenhouse. Photovoltaic systems can be classified into two categories: stand-alone systems and grid-connected systems.