Agricultural Pesticide Spraying Robot
Keywords:
ESP32, pesticide spraying robot, precision agriculture, L298N motor driver, DHT11 sensor, automationAbstract
This paper presents the design and development of a smart agricultural pesticide spraying robot based on the ESP32 microcontroller platform. The system integrates an L298N motor driver for mobility, a DHT11 temperature and humidity sensor for environmental monitoring, and a DC water pump motor for pesticide spraying. The proposed system automates pesticide spraying to minimize human exposure to harmful chemicals and improve resource efficiency. The robot operates semi-autonomously, monitoring environmental conditions in real time and activating the spray system as needed. The solution aims to support precision agriculture through conditional pesticide application and remote operability.
📊 Article Downloads
References
S. N. . Divekar and M. K. . Nigam, “Machine Learning Based Dynamic Band Selection for Splitting Auditory Signals to Reduce Inner Ear Hearing Losses”, IJRITCC, vol. 11, no. 6, pp. 71–78, Jul. 2023
Divekar S, Nigam MK. (2022). Minimize Frequency Overlapping of Auditory Signals using Complementary Comb Filters. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(3), 333-336.
Prof. Sudhir N. Divekar, Ankita. A. Shinde, Rohini. R. Mulay, Pooja. V. Jaybhaye, "Real Time Bridge Monitoring System", International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN : 2394-4099, Print ISSN : 2395-1990, Volume 7 Issue 3, pp. 406-411, May-June 2020. Journal URL : http://ijsrset.com/IJSRSET2073100
B. V. Aishwarya, G. Archana, and C. Umayal, “Agricultural robotic vehicle–based pesticide sprayer with efficiency optimization,” in Proc. 2015 IEEE Technological Innovations in ICT for Rural Development and Agriculture (TIAR), Chennai, India, 2015, pp. 59–65. DOI: 10.1109/TIAR.2015.7405632
F. A. Auat Cheein and R. J. Carelli, “Agricultural robotics: unmanned robotic service units in agricultural tasks,” IEEE Ind. Electron. Mag., vol. 7, no. 3, pp. 48–58, Sept. 2013. DOI: 10.1109/MIE.2013.2272682
E. Ozgul and U. Celik, “Design and implementation of semi autonomous anti pesticide spraying and insect repellent mobile robot for agricultural applications,” in Proc. 2018 IEEE 5th Int. Conf. Electrical and Electronics Engineering (ICEEE), Istanbul, Turkey, 2018, pp. 233–237. DOI: 10.1109/ICEEE.2018.8453640
C.–M. Wu and J.–T. Lu, “Implementation of remote control for a spraying robot,” in Proc. 2017 IEEE Int. Conf. Appl. Sys. Innovation (ICASI), Sapporo, Japan, 2017, pp. 1010–1013. DOI: 10.1109/ICASI.2017.7988627
P. Patel and B. Sahoo, “An electrostatic nozzle–enhanced spraying system for agricultural robotics,” IEEE Trans. Ind. Electron., vol. 64, no. 6, pp. 4751–4761, June 2017. DOI: 10.1109/TIE.2016.2635164
W. M. T. D. Wijesundara, S. Gomes, R. Saldanha, and K. Perera, “Accurate crop spraying with RTK and machine learning on an autonomous field robot,” IEEE Robot. Autom. Lett., vol. 4, no. 3, pp. 287–294, Jul. 2023. DOI: 10.1109/LRA.2023.3248972
Y. Li, C. Xia, and J. Lee, “Vision based pest detection and automatic spray of greenhouse plant,” in 2009 IEEE Int. Symp. Industrial Electronics (ISIE), Seoul, Korea, 2009, pp. 920–925. DOI: 10.1109/ISIE.2009.5215490
T. Liu, B. Zhang, and J. Jia, “Electromagnetic navigation system design of the greenhouse spraying robot,” in Proc. 2011 2nd Int. Conf. Mechanic Automation Control Eng. (MACE), Wuhan, China, 2011, pp. 2140–2144. DOI: 10.1109/MACE.2011.5998263
A. Pratto and G. Ardigò, “Autonomous navigation and spraying in agricultural robotic platforms,” in Proc. 2020 IEEE Int. Conf. Robot. Autom. (ICRA), Paris, France, 2020, pp. 5582–5588. DOI: 10.1109/ICRA40945.2020.9197021
R. Kaur, S. Malhotra, and P. Sharma, “IoT based greenhouse monitoring and pest management using ESP32,” in Proc. 2021 IEEE Int. Conf. Comput. Commun. (ICCC), New Delhi, India, 2021, pp. 115–120. DOI: 10.1109/ICCC51436.2021.9473358
S. Liu, X. Ding, and Y. Zhao, “Real time variable rate spray control based on canopy sensing,” in Proc. 2018 IEEE Int. Conf. Agric. Eng. (CIGR AgEng), Turin, Italy, 2018, pp. 14–19. DOI: 10.1109/AgEng.2018.8320753
J. Rios, M. Jiménez, and F. Ramos, “Sensor integration in ESP32 based autonomous agricultural robot,” IEEE Sens. J., vol. 21, no. 7, pp. 8470–8478, Apr. 2021. DOI: 10.1109/JSEN.2021.3057715
H. Wang, X. Sun, and L. Meng, “Machine vision–based weed detection for precision spraying on unmanned ground vehicles,” IEEE Trans. Ind. Inform., vol. 16, no. 12, pp. 7505–7514, Dec. 2020. DOI: 10.1109/TII.2019.2948210
K. Suzuki, N. Sasaki, and M. Watanabe, “SLAM based navigation in autonomous agricultural vehicles,” in Proc. 2019 IEEE Int. Conf. Robot. Biomimetics (ROBIO), Dali, China, 2019, pp. 1820–1825. DOI: 10.1109/ROBIO49542.2019.8968751
L. Cheng, Y. Li, and J. Zhang, “Kalman filter–based multi sensor fusion in agricultural spraying robots,” IEEE Trans. Robot. Autom., vol. 36, no. 2, pp. 530–539, May 2022. DOI: 10.1109/TRO.2020.2964867
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
