Paul et al. (2026) A Smart Irrigation System for Agriculture Land Utilizing Image Processing and AI

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Surajit Kumar Paul, Raktim Acharjee, Binoy Das
• DOI: 10.1007/978-981-96-9932-2_19

Research Groups

• Department of ECE, NIELIT Agartala, Agartala, Tripura, India
• Department of CSE, NIELIT Agartala, Agartala, Tripura, India

Short Summary

This paper presents an intelligent irrigation system for agricultural land, integrating image processing, AI, and soil moisture sensors to optimize water usage. Field tests demonstrated significant water savings of up to 40% while preserving crop health.

Objective

• To develop an intelligent irrigation system for agricultural land that minimizes water usage by monitoring crop moisture levels and only irrigating when necessary, utilizing image processing and artificial intelligence.

Study Configuration

• Spatial Scale: Agricultural land, distributed across an agricultural area, distinct areas within an agricultural field.
• Temporal Scale: Regular measurements by sensors, real-time data processing, dynamic scheduling of irrigation based on current needs.

Methodology and Data

• Models used: Artificial intelligence (AI), machine learning, image processing algorithms, decision-making criteria for irrigation scheduling, cloud-based platform for remote data analysis and control.
• Data sources: Interconnected soil moisture sensors, satellite or aerial photographs, real-time crop water requirement data.

Main Results

• The developed smart irrigation system successfully monitors crop moisture levels and plant health.
• Field testing demonstrated notable water savings of up to 40% compared to traditional irrigation methods.
• The system effectively preserved crop health while achieving significant water conservation.
• It determines optimal irrigation schedules and durations for distinct areas within an agricultural field based on real-time data.

Contributions

• Development of a novel intelligent irrigation system that integrates image processing, AI, soil moisture sensors, and a cloud-based platform for precise and efficient water management in agriculture.
• Demonstrated significant water savings (up to 40%) in field tests, offering a sustainable solution for optimizing water resources in agriculture, particularly in resource-constrained regions.
• Provides a scalable and efficient approach to optimize irrigation by applying water only when and where crops require it, based on real-time data and crop water requirements.

Funding

• Not specified in the provided text.

Citation

@article{Paul2026Smart,
  author = {Paul, Surajit Kumar and Acharjee, Raktim and Das, Binoy},
  title = {A Smart Irrigation System for Agriculture Land Utilizing Image Processing and AI},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-981-96-9932-2_19},
  url = {https://doi.org/10.1007/978-981-96-9932-2_19}
}