## 1 Introduction  
This research proposes an autonomous greenhouse system which has a sunlight reflector board (RB). Here, a greenhouse refers to an autonomous farm that can manage the growing environment of plants inside the greenhouse. In general, a greenhouse, which grows leafy vegetables, utilizes artificial light, such as LED light, as a supplementary source of light. However, artificial light cannot be used as the main source of light for photosynthesis for various kinds of plants. In our article, we utilize natural sunlight, instead of artificial light, for growing plants inside a greenhouse. Since the proposed greenhouse uses natural sunlight, one can grow various kinds of plants which cannot grow under artificial light. We develop an autonomous system to control the amount of sunlight inside the greenhouse by controlling the angle of the RB, which is located in front of the greenhouse. We periodically control the RB of sunlight, so that the optimal sunlight amount, which is set by the operator, can be provided for the plants inside the farm. We acknowledge that artificial light can be used as a supplementary source of light in cloudy days. There are many papers on smart solar tracking systems. Refs. [1; 2; 3] addressed smart solar tracking systems, which can increase the output power of solar panels. The solar tracking system in [2] utilized the difference between the reading of the light sensors on the top and bottom of the solar panel. When the difference is equal to zero, the position of sunlight is precisely in the middle of the solar panel. The goal of a solar tracking system is to arrange the solar panel, so that the panel is perpendicular to the sunlight direction. In this way, one can maximize the output power of solar panels. Our research is distinct from solar tracking systems [1; 2; 3], since we control the RB of sunlight, so that the optimal sunlight amount, which is set by the operator, can be provided for the plants inside the farm. There are many papers on monitoring smart farm systems [4; 5; 6; 7; 8]. Ref. [9] addressed automated agricultural processes (seeding, ploughing, irrigation, planting, fertilizing, weeding, or harvesting) using a programmable logic controller and zigbee network. Ref. [10] built a greenhouse system using low-power bluetooth and low-power wide area network communication modules including a wired communication network. Ref. [11] studied weather station and mobile data logging type monitoring. Ref. [5] utilized the Internet of Things, wireless networks, and message queuing telemetry tracking to monitor the agricultural environment in real time. Ref. [6] addressed a smart farm system including the monitoring of soil moisture and atmospheric sensors (temperature and relative humidity). Ref. [7] proposed a strategy for smart irrigation to optimize water consumption and to provide a remote control and monitoring of the irrigation system. Ref. [8] proposed to implement a wireless sensor network connected to a central node, which is connected to a central monitoring station. Ref. [12] proposed a trust model for monitoring humidity and moisture in agricultural environments. Moisture content (MC) detection plays a vital role in the monitoring and management of living trees. Targeting the drawbacks of high energy consumption, low practicability, and poor sustainability in the current field of living tree MC detection, Ref. [13] addressed an ultra-high-frequency radio frequency identification (UHF RFID) sensor system based on a deep learning model, with the main goals of non-destructive testing and high-efficiency recognition. Ref. [14] addressed the application of Internet-of-Things (IoT) technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time, while the mobile phone app uses the cloud platform as a monitoring center. Ref.