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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62771

ECOIRRIGATE: AI-Driven Smart Irrigation System for Sustainable Vertical Farming

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62771

Volume:

10 |

5 |

May 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62771

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.

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Authors

SANJANA D M

SANJANA D M

Computer science - AIML

Jain university

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SB

Shajahan Basheer

Computer science

Jain university

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JA

Jahnavi A

CSBS

Jain university

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DP

Deekshitha P

aerospace

Jain university

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PLK

P L Kerthanan

Computer science

Jain university

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Abstract

Water shortage and in efficient irrigation were the major problems in modern agriculture especially in systems like vertical farming. Traditional irrigation methods usually follow fixed timings or schedule and do not adjust based on the real time environmental conditions which often leads to the unnecessary water usage and also the lower efficiency. Our project presents ECOIRRIGATE, an AI based smart irrigation system that helps us water more efficiently were water loss is less and the investment is also less which needs is eventually to more efficiency and farmer friendly. The system uses a Random Forest model to predict soil moisture and a Logistic Regression model to predict whether it will rain or not. Based on these predictions, it designs whether irrigation should be turned on or off ensuring that water is used much more efficiently. To keep the system cost effective, it is developed using a simulation that mimics real sensor data such as soil moisture, temperature and humidity. A simple dashboard is also included here to display live data, predictions and irrigation status, whether it's on or off. The results show that usage of this system significantly reduces unnecessary irrigation compared to traditional methods. So, ECOIRRIGATE shows how combining AI with smart decision making can improve water wastage and support and sustainable and budget friendly farming practices.

KEYWORDS

Keywords: Artificial Intelligence (AI) Smart Irrigation Vertical Farming Systems Machine Learning Random Forest Model Soil Moisture Estimation Rainfall Prediction Precision Agriculture Sustainable Resource Management

Cite This Article

SANJANA D M, Shajahan Basheer, Jahnavi A, Deekshitha P, P L Kerthanan, "ECOIRRIGATE: AI-Driven Smart Irrigation System for Sustainable Vertical Farming," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-7, May 2026. https://doi.org/10.55041/IJSREM62771

Full Article/Conclusion

The research presents ECOIRRIGATE, which functions as an artificial intelligence smart irrigation system that improves water management for vertical farming operations. The system achieves water conservation through its three components which include soil moisture prediction and rainfall forecasting and intelligent irrigation scheduling to provide optimal growing conditions. The simulation-based implementation demonstrates that advanced irrigation systems can be developed without relying on expensive hardware components, making the solution cost-effective and scalable. The proposed system contributes to sustainable agriculture and demonstrates how artificial intelligence serves as a critical tool for precision farming and resource optimization efforts.

References

1.     Z. Gu et al., “Neural network soil moisture model for irrigation scheduling,” Agricultural Water Management, 2021. 2.     Y. Saikai et al., “Deep reinforcement learning for irrigation scheduling,” PLOS Water, 2023. 3.     S. Bhardwaj and R. Sharma, “Applications of AI in precision irrigation,” Computers and Electronics in Agriculture, 2024. 4.     M. Li et al., “Machine learning approaches for smart irrigation systems,” Information, 2024. 5.     P. K. Singh et al., “AI and climate-smart agriculture,” Environmental Science and Pollution Research, 2022. 6.     B. T. Agyeman et al., “Model predictive control for irrigation scheduling,” arXiv, 2023. 7.     B. T. Agyeman et al., “LSTM-based irrigation control model,” arXiv, 2021. 8.     R. Jones et al., “IoT-based smart irrigation system,” IEEE Access, 2022. 9.     A. Kumar and S. Patel, “AI-driven water optimization in agriculture,” Elsevier Smart Agriculture, 2023. 10.  H. Kim et al., “Precision irrigation using ML techniques,” Sensors, 2022. 11.  M. Raza et al., “Intelligent irrigation control using ANN,” IEEE Transactions on Automation Science, 2021. 12.  S. Verma et al., “Vertical farming and AI integration,” Sustainable Computing, 2023. 13.  D. Brown et al., “Climate-aware irrigation systems,” Agricultural Systems, 2022. 14.  L. Chen et al., “Hybrid ML models for soil moisture forecasting,” Water Resources Management, 2023.  

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Publication Date

5/7/2026

Peer Review Type

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Review Rounds: 2

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