Smart Agriculture Robot for Plant Disease Detection and Spraying
Keywords:
Smart Agriculture, Plant Disease Detection, Raspberry Pi 3B+, Node MCU ESP8266, Image Processing, IoT, Precision Farming, L298 Motor Driver, Automated Spraying, Machine Learning, Agricultural Robotics, Remote MonitoringAbstract
The agricultural sector is rapidly adopting automation and smart technologies to improve crop yield and reduce manual labor. This paper presents the design and implementation of a Smart Agriculture Robot capable of detecting plant diseases using image processing and performing targeted pesticide spraying. The system integrates a Raspberry Pi 3B+ with a 5MP camera module for capturing plant images, which are then analyzed using machine learning techniques to identify symptoms of plant diseases. Detected results are communicated to users via email reports and can be accessed on mobile or laptop devices. The robot’s movement is controlled using an ESP8266 Node MCU and a Node MCU car control app through Wi-Fi connectivity. An L298N motor driver drives four wheels based on remote commands. The system is powered by a 12V battery regulated through an LM7805 module. This integrated setup not only reduces farmers’ exposure to harmful chemicals by automating the spraying process but also facilitates early detection of diseases, thereby improving plant health and crop productivity. The proposed solution demonstrates a low-cost, scalable, and efficient method for precision agriculture using IoT and embedded systems.
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
Dhumale et al. (2021) – Smart Agricultural Robot for Spraying Pesticide with Image‑Processing‑based Disease Classification Demonstrated a robot capturing crop images to detect diseases and automatically spray pesticides using image processing.
Bouhaja et al. (2025) – Mobile Robot for Leaf Disease Detection and Precise Spraying (CNN + Path Planning). Implemented a Raspberry Pi–based robot with CNN disease detection and targeted spraying, achieving ~95 % precision and <1 cm navigation accuracy.
Mohtasim et al. (2023) – IoT-based Crop Monitoring and Disease Detection (Arduino UNO + ESP32 CAM). Designed an IoT system using camera feeds and image processing via OpenCV for early disease diagnosis in fields.
IJRPR (2022) – Agricultural Robot with Leaf Disease Detection Using Raspberry Pi A similar prototype using Raspberry Pi, camera, Arduino, and L298N driver for chassis control and disease detection..
IJERT(2025) –AgroBot: CNN‑Based Plant Disease Identification Employed CNNs trained on PlantVillage dataset for accurate plant disease classification.
IRJMETS(2024)–Smart IoT‑Based Multipurpose Robot. Developed a Raspberry Pi + CNN + L298N-based smart robot for plant disease detection in large farms.
Justia Patent (2023) – System and Method for Detecting Diseases Among Plants. Describes a four-wheeled mobile robot with GPS, camera, CNN processing, and wireless reporting capabilities.
Mdpi (2024) – Smart Sensors and Smart Data for Precision Agriculture. Review paper discussing IoT integration—sensors, analytics, precision agriculture benefits and challenges.
PMC (2025) – AI-IoT Based Smart Agriculture Pivot for Plant Disease Detection and Treatment. Discusses architecture combining AI, IoT, sensors, and automated actions within agriculture systems.
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.
