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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65138
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
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
Shreeyash College of Engineering and Technology
Mechanical Engineering
Shreeyash College of Engineering and Technology
Mechanical Engineering
Shreeyash College of Engineering and Technology
Mechanical Engineering
Shreeyash College of Engineering and Technology
Modern farming faces severe bottlenecks due to changing climate patterns, dwindling freshwater supplies, and a shrinking rural workforce. Traditional irrigation methods, like manual flooding or uncalibrated sprinklers, are highly inefficient, resulting in massive water waste, uneven moisture distribution, and lower crop yields. This paper introduces a low-cost, smart precision drip irrigation setup designed to eliminate these inefficiencies. Built around the 32-bit dual-core ESP32 microcontroller, the system combines real-time field monitoring with automated water delivery. The hardware layout integrates capacitive soil moisture sensors and DHT11/DHT22 ambient temperature/humidity modules to track local field dynamics. This data is continuously collected by the microcontroller and uploaded to cloud dashboards like Blynk, ThingSpeak, and Firebase. When soil moisture drops below a predefined agricultural threshold (30%–40%), the automated control logic activates a 12V DC pump via an opto-isolated relay. Field trials demonstrate that the system responds instantly to moisture shifts and operates reliably over long periods. Empirical results show a 66% to 70% drop in water consumption compared to flood irrigation, alongside reduced labor demands and energy costs. By utilizing corrosion-resistant capacitive sensors instead of traditional resistive probes, this design offers a highly durable, scalable, and practical approach to modern smart farming.
KEYWORDS
This study successfully demonstrates a low-cost, automated, IoT-driven precision drip irrigation platform built around the ESP32 microcontroller. Field data confirms that the automated logic successfully keeps soil moisture within the ideal agricultural window (30%–40%), adapting automatically to shifting ambient temperatures and humidity levels. By watering right at the plant root zone based on live data, the platform achieves a massive 66% to 70% drop in water use compared to traditional flood methods. Utilizing capacitive sensors completely solves the common issue of probe rust and corrosion, ensuring stable, reliable readings over long-term field use. The ESP32's built-in Wi-Fi chip kept data synced perfectly with cloud backends like Blynk, ThingSpeak, and Firebase, giving growers easy remote visibility and instant phone alerts. While initial component costs and internet coverage dependencies are notable constraints, the massive water savings, lower utility bills, and reduced manual labor make it incredibly valuable. Ultimately, this design offers a highly accessible, scalable, and efficient tool for sustainable farming, making it an excellent fit for water-scarce regions and modern precision agriculture.
[1] S. R. Nandhin, A. Balaji, and S. S. Ranjith, "IOT based smart drip irrigation system," International Journal of Engineering Research & Technology (IJERT), vol. 8, no. 6, pp. 45–48, Jun. 2019. [2] P. Suresh, J. Vijay Daniel, V. Parthasarathy, and R. H. Aswathy, "A state of the art review on smart irrigation system," International Journal of Computer Science and Information Technologies, vol. 5, no. 4, pp. 5331–5333, 2014. [3] M. Walid, M. Ashar, and M. H. Wahyudi, "Smart drip irrigation system based on IOT using fuzzy logic," INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, vol. 9, no. 1, pp. 1–12, 2025. [4] A. Kumar and G. P. Hancke, "A zigbee-based animal health monitoring system," IEEE Sensors Journal, vol. 15, no. 1, pp. 610–617, Jun. 2015. [5] R. J. Jadhav, R. R. Jadhav, and S. S. Patil, "IOT based automatic irrigation system," International Journal of Advanced Research in Computer and Communication Engineering, vol. 6, no. 6, pp. 242–245, Jun. 2017. [6] N. Gondchawar and R. S. Kawitkar, "IOT based smart agriculture," International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 6, pp. 838–842, Jun. 2016. [7] R. S. K. Reddy and T. S. Reddy, "IOT based smart irrigation system using soil moisture sensor," International Journal of Scientific Research in Engineering and Technology, vol. 7, no. 4, pp. 112–116, 2018. [8] M. S. Memon, E. Ahmed, and S. Shah, "An efficient smart irrigation system based on dual-core ESP32 and edge computing nodes," IEEE Transactions on Precision Agriculture, vol. 14, no. 2, pp. 145–153, Aug. 2023. [9] K. L. Sharma and V. B. Kumar, "Capacitive vs resistive soil sensing technology: A comparative reliability analysis for long-term farm instrumentation," Sensors and Actuators A: Physical, vol. 312, Art. no. 112104, Oct. 2022. [10] T. N. Gopinath and R. Anandan, "Cloud telemetry protocol optimization for resource-constrained IoT agricultural gateways," Journal of Network and Computer Applications, vol. 189, pp. 44–56, Jan. 2024.
Publication Date
6/18/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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