Real-Time Crop Monitoring and Precision Spraying Using Smart Agricultural Drone

Abstract

The use of unmanned aerial vehicles (UAVs) in agriculture has introduced a revolutionary way to improve farm productivity, precision, and crop health monitoring. This project focuses on the design and development of a quadcopter drone tailored for precision farming tasks. The drone employs a lightweight, durable carbon-fiber frame that ensures stable operation under varying field conditions. Its navigation is managed by a Pixhawk flight controller, supported by an onboard Raspberry Pi system. A Pi Camera connected to the Raspberry Pi captures real-time images of crop leaves, which are processed through trained image recognition models to identify two common leaf diseases. Depending on the diagnosis, the drone activates one of two dedicated pesticide pumps to administer targeted treatment, thereby minimizing chemical waste and avoiding unnecessary spraying. By merging aerial mobility, intelligent vision systems, and automated decision-making, the proposed system aims to reduce environmental impact, cut costs, and increase crop yield through sustainable and precision-based practices.

Introduction

The project developed a quadcopter drone equipped with a Pixhawk flight controller, Raspberry Pi, Pi Camera, and dual-pump pesticide system for precision agriculture. It combines autonomous flight with real-time crop health analysis, enabling disease detection and targeted pesticide application, reducing chemical use and costs while improving sustainability. Field tests showed effective scanning, disease identification, and selective spraying.

Future improvements include advanced imaging sensors (multispectral, thermal), enhanced AI for broader disease detection, higher navigation accuracy (RTK GPS or visual-inertial odometry), modular payloads, extended energy solutions, cloud connectivity for data-driven farming, and safer operation through obstacle avoidance and UAV regulation compliance. These enhancements could transform the system into a fully autonomous, multipurpose agricultural assistant, boosting efficiency, productivity, and sustainable farming practices.

Conclusion

The development of a quadcopter drone with a Pixhawk flight controller, Raspberry Pi, Pi Camera, and a dual-pump pesticide spraying system offers a practical solution for precision agriculture. By combining autonomous navigation with real-time analysis of crop health, the drone can perform essential farming tasks like disease detection and selective pesticide spraying with little human involvement. The Pixhawk controller provides stable flight, accurate waypoint following, and reliable missions. The lightweight carbon fiber frame improves endurance, maneuverability, and strength in changing field conditions. A key part of this project is the integration of the Raspberry Pi with onboard image processing. Using a Pi Camera and a pre-trained image classification model, the system can detect two types of leaf diseases during flight in real time. Based on this analysis, it applies the correct pesticide directly on the affected plants through two micro pumps. This minimizes chemical use and prevents unnecessary treatment of healthy crops. This focused approach not only lessens environmental impact but also improves resource use, reducing costs for farmers. Field tests confirmed that the quadcopter can autonomously scan crops, identify diseased areas, and carry out selective spraying effectively. The modular design allows for future upgrades, such as larger tanks, better sensors, or more advanced disease detection methods. Overall, this project shows how smart drone systems can significantly boost agricultural productivity, support sustainable farming, and provide farmers—both small and large—with data-driven tools for crop management. The use of a quadcopter equipped with a Raspberry Pi, Pi Camera, and a dual-pump pesticide system represents an exciting step toward smarter, autonomous farming. However, there are still areas for future improvement to expand the system\'s capabilities, efficiency, and practicality. One major area for growth is the addition of advanced imaging technologies like multispectral, hyperspectral, or thermal cameras. These sensors, along with AI-based analysis on the Raspberry Pi, could provide better assessments of crop health, early disease detection, soil condition monitoring, and irrigation planning. Enhancing the onboard machine learning could help identify a broader range of plant diseases and stress conditions, allowing for better-informed and timely farming actions. Improving navigation accuracy is also crucial. Adding RTK (Real-Time Kinematic) GPS or visual-inertial odometry would enable centimeter-level positioning accuracy. This level of detail is especially helpful for precision spraying, automated seed planting, or operating in areas where traditional GPS is less reliable. Higher accuracy could significantly reduce chemical use while improving treatment precision. The modularity of the system could also be improved to allow for interchangeable payloads. For instance, the current spraying mechanism could be swapped for seed dispensers, imaging modules, or environmental sensors. This change would let the same quadcopter serve multiple purposes during the farming cycle. Improving the efficiency and weight distribution of the spraying system would also enhance battery life and flight time. Energy solutions like solar panels or automated docking stations could support extended or continuous operation, which is particularly valuable for large farms. A hybrid power system or wireless charging at base stations could further minimize downtime between missions. On the software side, linking the Raspberry Pi to cloud platforms or IoT systems would enable remote monitoring, centralized data storage, and AI-assisted decision-making. This connection would support real-time crop analytics, historical performance tracking, and mission planning for multiple fields from a single dashboard. To make such systems more accessible to small and medium-scale farmers, future efforts could focus on creating low-cost, user-friendly drone kits with simplified interfaces and multilingual mobile apps. Training programs and local maintenance support could encourage adoption in rural and underserved areas. Finally, following UAV regulations and safety standards will be increasingly important. Adding ADS-B transponders or basic obstacle detection and avoidance systems would ensure safe operation in shared airspace and facilitate future management of autonomous fleets. In conclusion, while the current quadcopter system provides an effective solution for targeted pesticide application and disease detection, future improvements in hardware, AI, power systems, and connectivity could transform it into a fully autonomous, multipurpose agricultural assistant. These advancements could be key to achieving scalable, sustainable, and data-driven farming across various agricultural landscapes.

References

[1] Zhang, C., & Kovacs, J. M. (2012). \"The application of small unmanned aerial systems for precision agriculture: a review.\" Precision Agriculture, 13(6), 693-712. o Discusses the role of UAVs in agriculture and highlights research gaps in sensor integration and flight stability. [2] Colomina, I., & Molina, P. (2014). \"Unmanned aerial systems for photogrammetry and remote sensing: A review.\" ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. o Reviews UAV platforms and payloads, emphasizing the importance of GPS and telemetry in agricultural mapping. [3] González, L. F., Montes, G. A., Puig, E., Johnson, S., Mengersen, K., & Gaston, K. J. (2016). \"Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation.\" Sensors, 16(1), 97.

Copyright

Copyright © 2025 Mr. Pomnar Prashant Shantaram, Mr. DhingeTanmay Manish, Ms. Nikam Shraddha Pramod, Mr. Gunjal Siddhesh Vishwas, Ms. P. S. Patil, Mr. N. R. Thakare, Dr. V. A. Wankhede. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.