M. et al. (2026) AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture

Identification

• Journal: International Journal for Research in Applied Science and Engineering Technology
• Year: 2026
• Date: 2026-09-10
• Authors: Gowri M., Boomika M., S. Rakshana, Rubali R.
• DOI: 10.22214/ijraset.2026.84727

Research Groups

• Department of Bachelor of Information Technology, Annai Womens College, Karur, TamilNadu, India
• Department of BCA IT, Annai Womens College, Karur ,India

Short Summary

This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework for real-time multi-parameter soil tracking and predictive smart irrigation in agriculture. The system demonstrates a reduction in total water consumption while maintaining optimal volumetric soil water content.

Objective

• Investigate the feasibility of using AIoT for precision agriculture to optimize water usage and crop yields.
• Develop an autonomous, closed-loop AIoT architecture for real-time soil moisture management.

Study Configuration

• Spatial Scale: Field-scale (90-day testbed)
• Temporal Scale: Real-time monitoring with 24-48 hour predictive forecasting

Methodology and Data

• Models used: Long Short-Term Memory (LSTM) neural networks
• Data sources: Sensor telemetry data from capacitive soil moisture sensors, environmental probes, and soil pH sensor

Main Results

• The proposed AIoT framework achieved a 28% - 35% reduction in total water consumption.
• The LSTM predictive engine demonstrated strong performance on temporal soil moisture trends with an RMSE of volumetric water content ().
• The system maintained optimal volumetric soil water content, preventing both drought stress and root rot from over-saturation.

Contributions

• Original value of the article with respect to existing literature lies in its end-to-end AIoT framework combining low-power hardware sensor nodes, lightweight MQTT communications, and cloud-based LSTM predictive neural networks for precision agriculture.
• The system addresses key agricultural water and energy management challenges by transitioning from reactive irrigation to data-driven proactive forecasting.

Funding

• This research was funded by [project/program name] with reference code [reference code].

Citation

@article{M2026AIOT,
  author = {M., Gowri and M., Boomika and Rakshana, S. and R., Rubali},
  title = {AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture},
  journal = {International Journal for Research in Applied Science and Engineering Technology},
  year = {2026},
  doi = {10.22214/ijraset.2026.84727},
  url = {https://doi.org/10.22214/ijraset.2026.84727}
}