IOT Based Farming Robot Using ESP32 for Smart Agriculture Application

  The development of the Bluetooth-operated agricultural bot marks a significant step toward democratizing automation in modern farming. This research successfully demonstrates how a compact, low-cost prototype can integrate ploughing, seeding, and spraying operations into a single robotic unit governed by an ESP32 microcontroller. The system receives commands via a smartphone application, allowing easy operation without specialized technical knowledge. Experimental evaluations confirmed that the prototype achieves accurate motion control, efficient water and seed distribution, and sustainable energy utilization through solar support. The model also minimizes direct human exposure to pesticides, thereby promoting safer and more eco-friendly agricultural practices. Compared with conventional manual methods, the bot presented substantial improvements in time efficiency and uniformity of operations while lowering labor dependency. Through modular design and renewable power integration, this work demonstrates the feasibility of automated, low-maintenance agricultural systems suitable for small-scale farmers. The proposed framework can serve as a foundation for future enhancements—such as adding AI-based crop health monitoring, GPS-assisted navigation, and cloud data analytics—to move closer to the vision of comprehensive precision  agriculture. Ultimately, the study validates that robotic solutions, when designed with affordability and sustainability in mind, can transform conventional farming into a more productive, efficient, and environmentally responsible practice.

1.      Upadhyaya, S. K., Kulkarni, P., & Patil, V. (2018). Agricultural Robotics: A Review. International Journal of Engineering Science and Computing, 8(5), 17356– 17362. 2.      Patil, S. S., Patil, V. V., & Bhalekar, V. S. (2020). Advancements in Autonomous Agricultural Robotics: A Review. International Journal of Recent Technology and Engineering, 8(6), 2050–2056. 3.      Moreno, M. V., Durán-Ros, M., & Rial-Rivas, M. E. (2018). Smart Farming: Including the Use of Sensors for the Detection of Pests and Diseases in Crops. Sensors, 18(12), 4312. 4.      Bhandari, M., & Garg, S. K. (2019). Wireless Sensor Networks for Smart Agriculture: A Review. Journal of Sensor and Actuator Networks, 8(2), 27. 5.      Dai, N. (2009). Development and Application of Agricultural Robot. Agricultural Research, 31(2), 241–243. 6.      Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693– 712. 7.      Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. 8.      Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80. 9.      Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09. 037