# Farm Monitoring System with Drones and Optical Camera Communication
## 6 Conclusions  
In this study, we introduced a sensor network system for farm monitoring that combines drones and OCC. The proposed system allows for high-density sensing at a reduced cost. By using OCC, the system collects data from multiple sensor nodes spread across agricultural fields. This approach minimizes the need for expensive communication infrastructure and addresses the challenge of deploying high-density sensor networks in agriculture. We developed a trajectory control algorithm to optimize data collection. This algorithm improves communication efficiency between drones and sensor nodes. It utilizes OCC's strength in point-to-multipoint communication by reducing interference between multiple light sources. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. Our preliminary experiments at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, demonstrated the feasibility of the system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On-Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under \(10^{-3}\) in the real-world environment. These results validate the system's potential to enhance agricultural practices with a cost-effective and scalable sensor network.  
The proposed system is a significant step toward promoting intelligent agriculture. It offers a practical solution for overcoming the challenges of cost and infrastructure. The system is highly adaptable and can be deployed in areas with limited infrastructure or without radio-based communication, making it suitable for diverse agricultural environments. A major benefit of the proposed system is its compatibility with existing agricultural drones, such as those used for spraying pesticides. These drones can be easily repurposed as receivers within the OCC communication framework. Additionally, the cameras used for OCC can provide visual crop monitoring, offering dual functionality for data collection and crop observation. Future work will focus on improving the system's capabilities, particularly in integrating self-localization features to enhance drone navigation and control.  
Figure 13: Bit error rate with threshold of \(1\times 10^{-3}\).  
**Author Contributions:** Validation, S.K.; Investigation, S.K.; Writing--original draft, S.K.; Writing--review & editing, Y.N.; Supervision, Y.N.; Project administration, N.Y. and Y.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** A part of this work was supported by JSPS KAKENHI Grant Number JP20H04178 and JST, Presto Grant Number JPMJPR2137, and GMO Foundation, Japan.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to express their gratitude to Idomoto Farm at Kamitonda, Wakayama, Japan, and Sasaki Farm, Chitose, Japan, for their cooperation and valuable discussions throughout the experiments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.