Jabber et al. (2026) Design and Development of an AI-Powered Farm Environment Monitoring System

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Zainab Kadum Jabber, V. Sanjay, Sinan Adnan Diwan, Zainab R. Hadi, Ahmed J. M. Almihi
• DOI: 10.1007/978-981-96-9184-5_25

Research Groups

• Department of Computer Engineering Techniques, College of Engineering and Technology, Al-Mustaqbal University, Babylon, Iraq
• Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India
• College of Computer Sciences and Information Technology, Wasit University, Wasit, Iraq
• Najaf Health Directorate, Al-Hakim General Hospital, Pollution Control Unit, Najaf, Iraq
• Department of Artificial Intelligence Engineering Techniques, College of Technical Engineering, Alnoor University, Mosul, Nineveh, Iraq

Short Summary

This study designs and implements an AI-powered Farm Environment Monitoring System (FEMS) integrating IoT sensors and machine learning for real-time farm management. Field trials demonstrated significant reductions in water and fertilizer usage, alongside notable improvements in crop yield, promoting sustainable agriculture.

Objective

• To design and implement an AI-powered Farm Environment Monitoring System (FEMS) that leverages IoT sensors and machine learning algorithms to provide real-time actionable insights, optimize resource usage, minimize environmental impact, and improve agricultural productivity and sustainability, particularly for small and medium-scale farmers.

Study Configuration

• Spatial Scale: Field trials conducted in diverse farming environments.
• Temporal Scale: Real-time monitoring and analytics, enabling real-time decision-making.

Methodology and Data

• Models used: Machine learning algorithms for data analysis and generating actionable insights.
• Data sources: Cost-effective Internet of Things (IoT) sensors monitoring essential environmental parameters including soil moisture, temperature, humidity, and light intensity.

Main Results

• The developed FEMS significantly reduced water usage in agricultural applications.
• The system led to significant reductions in fertilizer usage.
• Notable improvements in crop yield were observed during field trials.
• FEMS offers affordability, scalability, and real-time decision-making capabilities, making it an inclusive solution for various agricultural applications.

Contributions

• Presents a practical, cost-effective, and scalable AI-powered Farm Environment Monitoring System (FEMS) that addresses the limitations of existing smart farming solutions, such as lack of real-time adaptability and financial inaccessibility.
• Integrates IoT sensors with machine learning algorithms to provide actionable insights for precision agriculture, including predicting optimal irrigation schedules, detecting potential crop stress, and forecasting environmental changes.
• Demonstrates tangible benefits in resource optimization (water and fertilizer) and crop yield improvement through empirical field trials.
• Offers a roadmap for scalable, data-driven, and sustainable farm management practices, particularly beneficial for small and medium-scale farmers.

Funding

• Funding information is not explicitly provided in the available paper text.

Citation

@article{Jabber2026Design,
  author = {Jabber, Zainab Kadum and Sanjay, V. and Diwan, Sinan Adnan and Hadi, Zainab R. and Almihi, Ahmed J. M.},
  title = {Design and Development of an AI-Powered Farm Environment Monitoring System},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-981-96-9184-5_25},
  url = {https://doi.org/10.1007/978-981-96-9184-5_25}
}