Sripriya et al. (2026) AI-Powered Intelligent Irrigation Management with Real-Time Alerts and Forecasting for Sustainable Agriculture

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: N. Sripriya, R. Thiagarajan
• DOI: 10.1007/978-3-032-10664-3_33

Research Groups

• Prathyusha Engineering College, Chennai, Tamil Nadu, India
• Veltech Multitech Dr. RR & Dr. SR Engineering College, Chennai, Tamil Nadu, India

Short Summary

This paper proposes an intelligent AI-controlled smart irrigation system for sustainable agriculture, integrating IoT-based real-time monitoring and deep learning to optimize water use, provide dynamic irrigation schedules, and deliver real-time alerts to farmers.

Objective

• To develop an intelligent AI-controlled smart irrigation system that leverages IoT-based real-time monitoring and deep learning techniques to optimize water use, provide real-time alerts, and forecast irrigation needs for sustainable agriculture, specifically addressing traditional irrigation inefficiencies in Thiruvallur, Tamil Nadu.

Study Configuration

• Spatial Scale: Localized application context in Thiruvallur, Tamil Nadu, India.
• Temporal Scale: Real-time monitoring and forecasting for irrigation scheduling, incorporating historical data for model training and adaptive planning.

Methodology and Data

• Models used: Long Short-Term Memory Convolutional Neural Network (LSTM CNN) for irrigation estimation, Generative Adversarial Networks (GANs) and Gaussian distribution for synthetic data generation, Reinforcement Learning for adaptive and dynamic irrigation scheduling.
• Data sources: IoT-based sensor network collecting real-time environmental data (soil moisture, temperature, humidity, rainfall, water level), historical data, and synthetically generated data.

Main Results

• The proposed system integrates AI, IoT, and real-time environmental adaptability to offer a scalable and cost-effective solution for optimal water resource utilization in smart agriculture.
• It provides precise irrigation estimations through an LSTM CNN model, with enhanced prediction robustness achieved by incorporating synthetic data generated via GANs and Gaussian distribution.
• An innovative AI-based alert system is included, capable of informing local farmers in Tamil through beeping alerts and voice notifications regarding irrigation scheduling.
• Irrigation scheduling is designed to be adaptive and dynamic, utilizing reinforcement learning to adjust plans based on both historical and real-time environmental data.

Contributions

• Development of a comprehensive AI-powered smart irrigation system that uniquely combines real-time IoT monitoring, advanced deep learning (LSTM CNN, GANs), and reinforcement learning for adaptive and dynamic irrigation scheduling.
• Introduction of an AI-based alert system with multilingual (Tamil) voice notifications, directly addressing communication needs of local farmers and enhancing practical system adoption.
• Focus on mitigating prevalent issues of over-irrigation or inadequate irrigation in specific agricultural regions (Thiruvallur, Tamil Nadu), offering a targeted solution for water scarcity and crop yield optimization.

Funding

• Not explicitly mentioned in the provided paper text.

Citation

@article{Sripriya2026AIPowered,
  author = {Sripriya, N. and Thiagarajan, R.},
  title = {AI-Powered Intelligent Irrigation Management with Real-Time Alerts and Forecasting for Sustainable Agriculture},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-3-032-10664-3_33},
  url = {https://doi.org/10.1007/978-3-032-10664-3_33}
}