# Farm Monitoring System with Drones and Optical Camera Communication
## 1 Introduction  
Agriculture is the cornerstone of human civilization, not only as the primary source of food but also as a provider of essential raw materials for numerous industries. It plays a crucial role in the economic development of nations, contributing significantly to their growth and stability [1, 2]. Despite this, a significant proportion of farmers worldwide continue to rely on traditional farming methods that often result in suboptimal yields. To address these challenges, the concept of smart agriculture, which aims to enhance productivity and efficiency through the automation of agricultural practices, has gained significant attention [3]. Smart agriculture refers to the integration of advanced technologies, such as robotics, artificial intelligence (AI), and the Internet of Things (IoT), into farming practices. These technologies enable the optimization of various agricultural processes, leading to enhanced efficiency and productivity. Key elements of smart agriculture include the deployment of autonomous ground vehicles, drones, and agricultural sensors that provide real-time data for precision farming. In recent years, the use of drones in smart farming systems has become increasingly widespread [4, 5]. Agricultural drones can follow preprogrammed flight paths autonomously, reducing the need for manual intervention. These drones are capable of performing critical agricultural tasks both during the day and at night, thereby alleviating the workload of farmers [6]. Drones are used for a variety of agricultural applications, including the spraying of pesticides and fertilizers as well as pest monitoring. One of the most valuable functions of drones is their ability to capture data through multispectral cameras, which can assess crop health, detect early signs of disease, and predict optimal harvest times [7; 8]. This technology allows farmers to remotely monitor the condition of their crops and take timely action to address potential issues. The continuous development of additional functionalities for drones holds great potential for further enhancing agricultural practices. Environmental monitoring using sensors to measure parameters such as temperature, humidity, soil moisture, and light intensity is a critical component of smart agriculture. These sensors enable precise management of crop growth, allowing farmers to make data-driven decisions to optimize agricultural productivity. The collection of sensor data is typically achieved through wireless communication, with protocols such as Low-Power Wide-Area Networks (LPWANs) being widely adopted due to their efficiency in transmitting data over long distances. However, despite the relatively low cost of sensors, the communication modules necessary for radio-based data transmission are often prohibitively expensive. This presents a significant challenge for the deployment of high-density sensor networks, where a large number of sensors are required to comprehensively monitor agricultural environments. The high cost of such infrastructure limits the scalability of sensor-based systems, particularly in large or resource-constrained farming operations. In addition to the cost barrier, geographical and infrastructural challenges also hinder the adoption of wireless sensor networks. In mountainous or remote regions, where communication infrastructure is sparse or nonexistent, radio-based transmission may fall outside the coverage area, further complicating the implementation of sensor networks in smart agriculture. Addressing these infrastructural gaps is essential to fully realize the potential of sensor-driven technologies in agricultural management. To address this issue, we propose an agricultural sensor network system that leverages Optical Camera Communication (OCC) technology. OCC is a communication method that uses visible light to transmit data between a light source and a camera. The idea behind the proposed system is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera.