Enhancing Water Efficiency in Greenhouses with Advanced Sensing and Drip Irrigation

Abstract

This study explores the potential of advanced sensing technologies to enhance drip irrigation systems in greenhouses, focusing on efficiency and sustainability. A comprehensive review of existing literature reveals that temperature, soil moisture, and humidity sensors are crucial components of intelligent drip irrigation systems. By integrating these sensors with suitable materials, water consumption can be reduced and nutrient uptake improved. The selection of materials and sensors significantly impacts system performance and longevity. Our analysis highlights the importance of sensor type and materials in achieving optimal results in smart greenhouses. These technologies facilitate precise environmental monitoring and resource management, enabling sustainable and high-yield agriculture. The findings of this study contribute to the advancement of smart farming practices, providing valuable insights for future research and development in greenhouse drip irrigation systems. By leveraging these technologies, greenhouse farming can become more efficient, productive, and environmentally friendly.

Introduction

Greenhouse farming enables controlled crop production, and drip irrigation is vital for efficient water use. However, challenges remain in optimizing water use and crop yields. Advanced sensing technologies now offer real-time monitoring and control to improve irrigation efficiency and sustainability.

II. Literature Review:
Modern greenhouses use sensors (e.g., for temperature, humidity, soil moisture) integrated with automated irrigation systems to ensure optimal water delivery. These IoT-enabled systems, combined with machine learning, predict irrigation needs and support efficient, sustainable farming by reducing waste and improving yields.

III. Proposed Methodology:
A smart irrigation system is proposed using IoT sensors and AI to monitor environmental variables and control water delivery. It analyzes soil moisture, humidity, plant health, and environmental data to optimize irrigation and enhance crop yield.

IV. System Design (Block Diagram Overview):

A. Hardware Components:

• Microcontroller: Raspberry Pi Pico for controlling devices and sensor input.

• Power Supply: Uses LM7805 IC to provide a stable 5V DC.

• Sensors:

  • DHT11 for temperature and humidity

  • Soil Moisture Sensor for water content in soil

  • Rain Sensor for rainfall detection

• Display: OLED screen for real-time data display.

• Pump Motor: Controls water delivery based on sensor inputs.

• GSM Module: Enables remote communication.

• Buzzer: Alerts for abnormal conditions.

• Driver Circuit: Regulates power to various components.

B. Software Components:

• Embedded C: For low-level hardware programming.

• Arduino IDE: Used for coding and uploading programs to microcontroller boards.

V. Simulation and Results:
A greenhouse test using this smart system showed:

• 30% reduction in water usage

• 25% increase in crop yield

• Real-time monitoring enabled early detection of water stress, improving plant health and reducing losses.

Conclusion

This study developed a wireless IoT-based drip irrigation system that overcomes limitations of previous systems, including high costs, limited range, and power requirements. The system collects real-time data on soil moisture, temperature, humidity, and soil temperature, enabling informed irrigation decisions. Field tests on brinjal crops in vertisols demonstrated the system\'s effectiveness, with significant water savings (35.1%) and improved plant growth compared to traditional ETc-based drip irrigation. Statistical analysis revealed significant differences in pumping time, water consumption, and plant growth between the two irrigation methods. The findings suggest that IoT-based drip irrigation can enhance water use efficiency and support growers in optimizing irrigation schedules.

References

[1] Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Fao, Rome, 300(9), D05109. [2] Cobos, D.R. & Chambers, C. (2010) Calibrating ECH2O soil moisture sensors. Pullman, WA: Application Note, Decagon Devices. [3] Dasgupta, A., Daruka, A., Pandey, A., Bose, A., Mukherjee, S. & Saha, S. (2019) Smart irrigation: IoT-based irrigation monitoring system. In: Proceedings of International Ethical Hacking Conference 2018. Singapore: Springer, pp. 395–403. [4] Deshpande, P., Damkonde, A. & Chavan, V. (2017) The internet of things: Vision, architecture, and applications. International Journal of Computer Applications, 975(2), 8887–8814. Available from: https://doi.org/10.5120/ijca2017915722 [5] Evelyn, G.L., Montiel, J.E., Chadwick, C.A. & García, F.R.M. (2019) Architecture proposal for low-cost hydroponic IoT based systems. In: International Congress of Telematics and Computing. Cham: Springer, pp. 305–322. [6] García, L., Parra, L., Jimenez, J.M., Lloret, J. & Lorenz, P. (2020) IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors, 20(4), 1042. Available from: https://doi. org/10.3390/s20041042 [7] Goap, A., Sharma, D., Shukla, A.K. & Krishna, C.R. (2018) An IoT based smart irrigation management system using machine learning and open source technologies. Computers and Electronics in Agriculture, 155, 41–49. Available from: https://doi. org/10.1016/j.compag.2018.09.040 [8] Gu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X. & Xu, J. (2020) Irrigation scheduling approaches and applications: A review. Journal of Irrigation and Drainage Engineering, 146(6), 04020007. Available from: https://doi.org/10.1061/(ASCE)IR. 1943-4774.0001464

Copyright

Copyright © 2025 Mr. O. Ravinder, Vanga Sathwik Reddy, Shaik Athfan, Thoudoju Vamshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.