Design and Development of an IoT Based Smart Pesticide Spraying Robot

Abstract

The experiment was conducted during the Rabi season of 2024–2025 at the Agronomy Main Research Farm, A.M. Reddy Memorial College, Narasaraopet, Palnadu District. A smart pesticide spraying robot was deployed to monitor and manage pest infestations across different crops, including tomato, brinjal, chilli, and cotton. The robot effectively identified pests such as aphids, whiteflies, and bollworms, adapting pesticide spraying quantities based on detection results. Observations indicated that pesticide application ranged from 150 mL to 250 mL, covering areas between 20 m² and 250 m², with corresponding battery usage and operational time. In cases of no pest detection, only monitoring activities were performed, demonstrating efficient energy and time management. Field results highlighted the robot\'s capability to optimize pesticide usage, reducing unnecessary spraying while maintaining pest control efficiency. The cost of the developed smart pesticide robot was ?9,000, making it an affordable solution with minimal maintenance costs. This innovation presents a promising approach toward sustainable, automated pest management in agricultural practices.

Introduction

The study focuses on developing an IoT-based smart pesticide spraying robot to enhance agricultural productivity through efficient pest management. Traditional pesticide application methods often lead to excessive chemical use, resulting in environmental hazards, economic losses, and potential harm to non-target organisms. The integration of smart technologies in agriculture has gained significant attention, offering solutions to these challenges.

A smart pesticide spraying robot was designed and deployed during the Rabi season of 2024–2025 at the Agronomy Main Research Farm, A.M. Reddy Memorial College, Narasaraopet, Palnadu District. The robot was equipped with pest detection capabilities, enabling targeted pesticide application based on real-time field data. Performance parameters such as pesticide quantity, coverage area, battery usage, and operational time were recorded to assess efficiency. Additionally, a cost analysis revealed a total setup cost of ?9,000 with minimal maintenance requirements. The implementation of such smart robotic systems offers a promising solution for achieving precise, eco-friendly, and cost-effective pest management in modern agriculture.

Objectives of the Study:

1. To design and develop an IoT-based smart pesticide robot.

2. To integrate sensors for real-time monitoring and precise pesticide application.

3. To minimize environmental impact.

Review of Literature:

The advent of the Internet of Things (IoT) has significantly transformed the agricultural sector, offering new opportunities to enhance productivity, sustainability, and efficiency. IoT technologies have found critical applications in precision pest management and smart irrigation. Researchers have extensively explored how integrating IoT with autonomous robots, drones, and artificial intelligence (AI) can optimize pesticide spraying and water management practices, leading to more sustainable agricultural operations.

Materials and Methodology:

The IoT-based smart pesticide-spraying robot features a 2-liter water tank, four wheels with 40 mm inner and 35 mm outer diameters, a durable 30 cm square cast-iron chassis, four durable black PVC nozzles that rotate 360 degrees, a 12V rechargeable battery, an Arduino Uno microcontroller, an HC-05 Bluetooth module, an L298N motor driver, DC motors, a relay, and jumper wires. These components were integrated to achieve efficient pest detection, precise pesticide application, and optimized resource utilization.

Methodology:

1. Robot Design and Fabrication: The robot chassis was constructed using a 30 cm square cast-iron frame to ensure durability and stability. Four wheels with 40 mm inner and 35 mm outer diameters were mounted to provide smooth and stable movement across different agricultural terrains.

2. Component Integration: Key components such as the 2-liter water tank, four 360-degree rotatable PVC nozzles, a 12V rechargeable battery, and DC motors were integrated into the chassis. The nozzles were designed to adjust spray sizes between 0.5 mm and 5 mm, providing flexibility in pesticide application.

3. Control System Development: An Arduino Uno microcontroller was used to manage all robotic operations. The Arduino was programmed to control the motion, spraying actions, and sensor data acquisition. An L298N motor driver was employed to manage the speed and direction of the DC motors.

4. Communication Setup: Wireless communication was established using the HC-05 Bluetooth module, enabling remote control and monitoring of the robot through a smartphone or computer interface.

5. Power Management: A 12V rechargeable battery supplied power to all robotic components. A relay module was used to safely control high-power devices like pumps and sprayers based on low-power microcontroller signals, ensuring efficient energy management.

6. Sensor Connectivity and Wiring: All electronic modules, sensors, and motors were interconnected using jumper wires for reliable signal transmission and power distribution. This setup ensured flexibility in prototyping and troubleshooting.

7. Field Deployment and Testing: The robot was deployed in the Agronomy Main Research Farm at A.M. Reddy Memorial College during the Rabi season of 2024–2025. It was tested across crops like tomato, brinjal, chilli, and cotton. Pest detection, pesticide usage, operational time, and battery consumption were systematically recorded.

8. Performance Evaluation: Observations were made regarding pesticide quantities (ranging from 150 mL to 250 mL), coverage areas (20 m² to 250 m²), and battery usage efficiency. In the absence of pest detection, the robot performed monitoring activities without pesticide application, demonstrating effective energy and time management.

9. Cost Analysis: The total development cost of the smart pesticide-spraying robot was calculated as ?9,000, highlighting its affordability and minimal maintenance requirements, making it suitable for large-scale deployment in agriculture.

Results and Discussion:

• Experimental Site: The experiment was conducted at the Agronomy Main Research Farm of A.M. Reddy Memorial College, Narasaraopet, located in Palnadu district. The study was carried out during the Rabi season of 2024–2025. The farm is positioned at 16° 10’ 25’’ N latitude and 79° 59’ 21’’ E longitude, at an altitude of 77 meters above sea level.

• Climate and Weather: Narasaraopet experiences a moderate climate. The monsoon usually starts in the last week of December and lasts until April. Summer temperatures range from 27°C to 45°C, while winter temperatures range between 15°C and 22°C. During the experiment, weather conditions such as temperature, humidity, rainfall, and sunshine hours were recorded.

Conclusion

The deployment of the IoT-based smart pesticide spraying robot in both controlled farm and field conditions demonstrated its significant potential to enhance agricultural efficiency and sustainability. By accurately detecting pests and adjusting pesticide application based on real-time data, the robot effectively minimized pesticide usage, thereby reducing environmental impact and conserving resources. The robot\'s ability to operate autonomously with optimized energy consumption, especially when no pests were detected, highlighted its efficiency in both time and power management. With its affordable setup cost and minimal maintenance requirements, this innovative solution offers a cost-effective alternative to traditional pesticide application methods. Overall, the smart pesticide spraying robot proves to be a valuable tool in modern agriculture, enabling precision pest management, resource conservation, and eco-friendly farming practices. The system\'s successful performance during the rabi season of 2024-2025 further underscores its potential for widespread adoption in sustainable agricultural practices.

References

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Copyright

Copyright © 2025 Badipati Chinna Babu, Chinnam Sandhya, Namburi Sunitha, Bhukya Hanuma Naik, Addanki Sushma Lahari, Mudavath Vamsi Naik. 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.