Khan et al. (2026) IoT—Integrated ML Framework for Automated Soil Quality Assessment

Identification

• Journal: Information systems engineering and management
• Year: 2026
• Date: 2026-01-01
• Authors: M. Javed Khan, Ashutosh Kumar Bhatt, Durgesh Pant, O. P. Nautiyal
• DOI: 10.1007/978-3-032-10122-8_3

Research Groups

• Columbia University, New York, USA
• School of Computer Science & Information Technology, Nainital, Haldwani, Uttarakhand, India
• Uttarakhand Council for Science and Technology, Dehradun, Uttarakhand, India

Short Summary

This chapter introduces an integrated Internet of Things (IoT) and Machine Learning (ML) framework for automated soil quality assessment, aiming to enhance precision agriculture. The proposed system utilizes a sensor network for real-time parameter monitoring, employs ML for soil health prediction and intervention recommendations, and was validated for reliability and adaptability in field trials in Uttarakhand, India.

Objective

• To develop and implement a comprehensive IoT-integrated Machine Learning framework for automated soil quality assessment to enhance precision agriculture and sustainable farming practices.

Study Configuration

• Spatial Scale: Field-level, regional (hilly regions of Uttarakhand, India), adaptable across diverse soil types and altitudes.
• Temporal Scale: Real-time, continuous monitoring of soil and environmental parameters.

Methodology and Data

• Models used: Machine Learning algorithms (specific algorithms not detailed) for predicting soil health metrics and recommending crop-specific interventions.
• Data sources:
  • Sensors: MQ135 (gas), MQ7 (carbon monoxide), MP304 (methane), DHT22 (temperature and humidity), DS18B20 (temperature), SEN049 (pH, moisture, nitrates).
  • Communication: GSM and GPS modules for data transmission.
  • Platform: Cloud-based platform for data storage, analysis, and remote access via web and mobile interfaces.

Main Results

• A comprehensive IoT-ML framework was developed for automated soil quality assessment, monitoring parameters such as moisture, pH, temperature, humidity, nitrates, methane, and carbon monoxide.
• Sensor data is transmitted via GSM and GPS to a cloud platform, enabling real-time monitoring and remote access.
• Machine Learning algorithms analyze sensor data to predict soil health metrics and provide crop-specific intervention recommendations.
• The system supports automated irrigation based on moisture thresholds, optimizing water usage and reducing manual labor.
• Field trials conducted in the hilly regions of Uttarakhand demonstrated the system's reliability and adaptability across diverse soil types and altitudes.
• The integration of cloud computing, AI, and remote sensing enhances decision-making, supports environmental monitoring, and enables scalable deployment, offering a cost-effective, data-driven solution for improved crop productivity.

Contributions

• Presents a novel, comprehensive framework integrating IoT, Machine Learning, cloud computing, AI, and remote sensing for automated soil quality assessment.
• Demonstrates the practical application and validation of the system in real-world, diverse agricultural environments (hilly regions of Uttarakhand, India).
• Offers a cost-effective and data-driven solution that promotes efficient resource management, optimizes water usage through automated irrigation, and improves crop productivity.
• Provides real-time monitoring and remote access capabilities, enhancing decision-making for farmers and agricultural stakeholders.

Funding

• Uttarakhand Science Education and Research Centre (USERC), Dehradun, Department of Science and Technology, Government of Uttarakhand (India).

Citation

@article{Khan2026IoTIntegrated,
  author = {Khan, M. Javed and Bhatt, Ashutosh Kumar and Pant, Durgesh and Nautiyal, O. P.},
  title = {IoT—Integrated ML Framework for Automated Soil Quality Assessment},
  journal = {Information systems engineering and management},
  year = {2026},
  doi = {10.1007/978-3-032-10122-8_3},
  url = {https://doi.org/10.1007/978-3-032-10122-8_3}
}