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This chapter presents a comprehensive framework integrating Internet of Things (IoT) and Machine Learning (ML) for automated soil quality assessment, aimed at enhancing precision agriculture and sustainable farming. The proposed system utilizes a network of sensors—including MQ135, MQ7, MP304, DHT22, DS18B20, and SEN049—to monitor key soil and environmental parameters such as moisture, pH, temperature, humidity, nitrates, methane, and carbon monoxide. Data collected from these sensors is transmitted via GSM and GPS modules to a cloud-based platform, enabling real-time monitoring and remote access through web and mobile interfaces. ML algorithms analyze sensor data to predict soil health metrics and recommend crop-specific interventions. The system supports automated irrigation based on moisture thresholds, reducing manual labor and optimizing water usage. Field trials conducted in the hilly regions of Uttarakhand demonstrate 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. This framework offers a cost-effective, data-driven solution for farmers and agricultural stakeholders, promoting efficient resource management and improved crop productivity. Future enhancements include expanded sensor networks, predictive analytics, and advanced classification models for soil and environmental profiling.

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IoT—Integrated ML Framework for Automated Soil Quality Assessment

  • 章节

  • 发表日期：

    2026年

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  • Muneer Khan,
  • Ashutosh Kumar Bhatt,
  • Durgesh Pant,
  • O. P. Nautiyal

摘要

This chapter presents a comprehensive framework integrating Internet of Things (IoT) and Machine Learning (ML) for automated soil quality assessment, aimed at enhancing precision agriculture and sustainable farming. The proposed system utilizes a network of sensors—including MQ135, MQ7, MP304, DHT22, DS18B20, and SEN049—to monitor key soil and environmental parameters such as moisture, pH, temperature, humidity, nitrates, methane, and carbon monoxide. Data collected from these sensors is transmitted via GSM and GPS modules to a cloud-based platform, enabling real-time monitoring and remote access through web and mobile interfaces. ML algorithms analyze sensor data to predict soil health metrics and recommend crop-specific interventions. The system supports automated irrigation based on moisture thresholds, reducing manual labor and optimizing water usage. Field trials conducted in the hilly regions of Uttarakhand demonstrate 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. This framework offers a cost-effective, data-driven solution for farmers and agricultural stakeholders, promoting efficient resource management and improved crop productivity. Future enhancements include expanded sensor networks, predictive analytics, and advanced classification models for soil and environmental profiling.