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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62257
Volume:
10 |
4 |
April 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Mechanical Engineering
R.V.R. & J.C. COLLEGE OF ENGINEERING
Mechanical Engineering
R.V.R. & J.C. COLLEGE OF ENGINEERING
Mechanical Engineering
R.V.R. & J.C. COLLEGE OF ENGINEERING
Mechanical Engineering
R.V.R. & J.C. COLLEGE OF ENGINEERING
Agriculture plays a vital role in food production and the economy, especially in developing nations like India. Traditional farming methods demand excessive manual labor, time, and resources. This paper presents the design, development, and experimental testing of a Solar Powered Multipurpose Agriculture Robot (SPMAR) aimed at automating key farming operations. The robot is built around an ESP32 microcontroller and powered by renewable solar energy. It integrates DC motors, an L298N dual H-bridge motor driver, a seed hopper, a mini submersible water pump, and a mechanical ploughing unit. The system operates in two modes — Automatic Mode (sensor-driven) and Manual Mode (Bluetooth/Wi-Fi remote control via a mobile application). Sensor data is transmitted to the ThingSpeak IoT cloud platform for real-time monitoring. Experimental results confirm that the robot successfully performs ploughing, seed sowing, drilling, and irrigation. The irrigation system achieved a 20% increase in soil moisture within 30 minutes and covered approximately 500 m² per water-tank cycle. Seed placement efficiency was 85–90%. Autonomous navigation achieved a 90% obstacle avoidance success rate. Overall, the system demonstrated a 23% reduction in water usage and a 3–4× increase in effective labor productivity compared to conventional methods.
KEYWORDS
This paper presented the design, development, and experimental validation of a Solar Powered Multipurpose Agriculture Robot built around the ESP32 microcontroller. The prototype successfully automated four core farming operations — ploughing, seed drilling, seed sowing, and irrigation — within a single solar- powered platform. The dual-mode operation (Automatic and Manual) provides farmers with the flexibility of both fully autonomous and remotely guided operation. Real-time field data (soil moisture, temperature, humidity) is continuously uploaded to the ThingSpeak IoT cloud, enabling data-driven farm management. Quantitative results demonstrated a 23% reduction in water consumption, an 85–90% seed placement efficiency, 4–6 cm ploughing depth, and a 3– 4× improvement in labor productivity relative to manual methods. The 90% obstacle avoidance rate confirms feasibility for real-field deployment, subject to further refinement on rough terrain. The modular hardware and software architecture is inherently scalable: battery and solar panel upgrades can extend per-charge field coverage to 1–2 acres, while fleet-management integration via a shared ThingSpeak dashboard can enable coordinated multi-robot operation on larger farms. Future enhancements include GPS-RTK autonomous navigation, camera-based AI crop disease detection, variable-rate fertilizer dispensing, and edge AI (TensorFlow Lite on ESP32-S3) for real-time pest identification. This work demonstrates that affordable, smart agricultural automation is a practical and deployable solution today, with significant potential to transform smallholder farming in India and similar developing economies.
[1] Shamshiri, R. et al. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1–14. https://doi.org/10.25165/j.ijabe.20181104.4278 [2] Duckett, T. et al. (2018). Agricultural Robotics: The Future of Robotic Agriculture. UK-RAS White Papers, EPSRC UK Robotics and Autonomous Systems Network. [3] Droukas, L. et al. (2022). A Survey of Robotic Harvesting Systems and Enabling Technologies. Journal of Intelligent & Robotic Systems, 107(2), 21. https://doi.org/10.1007/s10846-022-01793 [4] Rajendran, S. et al. (2023). Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control. Journal of Field Robotics, 41, 2247–2279. https://doi.org/10.1002/rob.22230 [5] Ahn, H. et al. (2018). An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest. arXiv preprint, IROS 2018. https://arxiv.org/abs/1807.03124 [6] Shamshiri, R. & Hameed, I.A. (2024). Mobile Robots in Digital Farming. Springer. [7] Vougioukas, S.G. (2019). Agricultural Robotics. Annual Review of Control, Robotics, and Autonomous Systems, 2, 365–392. https://doi.org/10.1146/annurev-control-053018- 023617 [8] Botta, A. et al. (2022). A Review of Robots, Perception, and Tasks in Precision Agriculture. Applied Mechanics, 3(3), 830–854. https://doi.org/10.3390/applmech3030049 [9] Fontani, A. et al. (2025). A Systematic Review of 59 Field Robots for Agricultural Tasks. Agronomy, 15(9), 2185. https://doi.org/10.3390/agronomy1509218 [10] Tanwar, V. et al. (2026). Agri-robotics in smallholder agriculture: A comprehensive review. Plant Science Today, 13(sp1), 01–09.
Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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