Agromist: Design and Development of an Aerial Pesticide Spraying Drone for Precision Agriculture

Abstract

Agriculture is very important for feeding the increasing number of people, and protecting crops effectively helps increase harvests and make farming more sustainable. Using traditional methods to spray pesticides is hard work, takes a lot of time, and can harm farmers\' health because of exposure to dangerous chemicals. To solve these problems, this paper introduces AgroMist, a drone that sprays pesticides from the air using a hexacopter. The system is made with a ZD850 hexacopter frame, a Cube Orange flight controller, a GPS module, brushless DC motors, electronic speed controllers, and a spraying system. The drone can be operated either by hand or automatically using Mission Planner software. It helps spread pesticide evenly and reduces waste. How it works is by using the flight controller to process signals and control the motor speed for steady flight. A pump system spreads the pesticide over the crops, and the air from the propellers helps the spray reach deeper into the plants. Test results show that the system flies steadily, moves accurately, and sprays effectively. This system makes farming safer, less tiring, and more productive. This work highlights how drone technology can help in precision agriculture.

Introduction

Across all the provided texts, a common theme is the use of modern intelligent systems to solve real-world problems in safety, healthcare, manufacturing, agriculture, and digital information processing. Each work focuses on replacing traditional, inefficient, or risky methods with automated, sensor-driven, or AI-based solutions.

In transportation safety, one study presents a smart driver assistance system that integrates eye-blink monitoring, alcohol detection, and fire sensing using a microcontroller-based platform. It automatically alerts the driver, disables ignition under unsafe conditions, and activates fire suppression, aiming to reduce road accidents.

In healthcare forecasting, another study uses machine learning models such as Random Forest and XGBoost to predict Influenza-Like Illness trends from historical surveillance data. It improves traditional reporting systems by providing faster, more accurate predictions through an interactive web-based platform.

In sustainable manufacturing, research on CNC machining emphasizes reducing energy consumption and carbon emissions through CAM-based toolpath optimization. It shows that optimized machining paths reduce idle time, machining duration, and overall electricity usage compared to conventional strategies.

Another manufacturing-focused study explores carbon reduction in CNC machining by developing theoretical models that link machining parameters, energy usage, and emissions, highlighting CAM optimization as a key strategy for sustainable production.

In fuel cell electric mobility, a PEM fuel cell-powered e-rickshaw system is proposed using a six-phase interleaved boost converter and a neural network controller. The system improves voltage regulation, reduces current ripple, and optimizes power extraction for efficient hydrogen-based transportation.

In robotics, a line-following robot with obstacle avoidance is developed using IR and ultrasonic sensors with a non-blocking state-machine approach. It enhances responsiveness and real-time decision-making by avoiding delays common in traditional designs.

In document digitization, an end-to-end system is presented for handwritten Malayalam recognition and translation. It combines YOLOv8 for text detection, CRNN-based OCR, linguistic correction, and neural machine translation to convert handwritten documents into English, addressing a major gap in Indic script digitization.

Finally, in agriculture, a hexacopter-based drone system (“AgroMist”) is designed for pesticide spraying. It improves efficiency, safety, and coverage using GPS-guided autonomous flight, stable multi-rotor design, and automated spraying mechanisms.

Overall, all studies emphasize automation, AI, and embedded system integration to improve efficiency, reduce human effort, enhance safety, and support sustainability across different domains.

Conclusion

The AgroMist drone system offers a smart and effective way to spray pesticides in farming. It makes farming easier by reducing the need for manual work, makes the process safer for people, and helps spread the pesticides more evenly and efficiently. By combining good hardware and software, the system keeps the drone flying smoothly, guides it accurately, and ensures the pesticide is spread uniformly. This system has successfully achieved the goals of boosting farm output and lowering the amount of chemicals used. Looking ahead, there are plans to improve the system with AI for watching crops closely, using better sensors, and upgrading the battery to make it work even better.

References

[1] D. Yallappa et al., “Development and evaluation of drone mounted sprayer for pesticide applications to crops,” IEEE GHTC, 2017. [2] U. M. R. Mogili and B. B. V. L. Deepak, “Review on application of drone systems in precision agriculture,” Procedia Computer Science, 2018. [3] G. P. Borikar et al., “Application of drone systems for spraying pesticides in advanced agriculture,” 2025.D. Yallappa et al., “Improving agricultural spraying with multi-rotor drones,” 2024. [4] S. Biswas et al., “Advancements in precision agriculture: pesticide spraying drones,” IRJMETS, 2023. [5] A. Rejeb et al., “Drones in agriculture: A review and bibliometric analysis,” 2022.

Copyright

Copyright © 2026 Smt. G. Divya Praneetha , Achikuchi V V S S Kasi Viswanath, Akkem Venkata Pavan, Ammenpalli Vijay Simha. 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.