Thingujam et al. (2025) From point sensing to intelligent systems: a comprehensive review on advanced sensor technologies for soil health monitoring

Identification

• Journal: Discover Sensors
• Year: 2025
• Date: 2025-12-20
• Authors: Umalaxmi Thingujam, D. M. Mary Synthia Regis Prabha, Animesh Ghosh Bag, Victor Thingujam, N. P. Darshan, Suman Dutta, Subrata Gorain
• DOI: 10.1007/s44397-025-00028-8

Research Groups

• Department of Soil Science and Agricultural Chemistry, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati (Central University), Sriniketan, West Bengal, India
• Department of Agriculture, Swami Vivekananda University, Barrackpore, Kolkata, India
• ICAR–Research Complex for Eastern Region-Farming System Research Centre for Hill and Plateau Region, Plandu, Ranchi, Jharkhand, India
• Department of Agricultural Extension, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati (Central University), Sriniketan, West Bengal, India
• Faculty Centre for Integrated Rural Development and Management, Ramakrishna Mission Vivekananda Educational and Research Institute, Kolkata, India
• Department of Agricultural Economics, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati (Central University), Sriniketan, West Bengal, India

Short Summary

This comprehensive review synthesizes recent advancements in soil sensor technologies, demonstrating their transformative potential for precision agriculture by enhancing the accuracy, specificity, and field-deployability of monitoring key soil parameters. It highlights the integration of these sensors into intelligent systems like IoT and WSNs, while also addressing persistent technological and scalability challenges for widespread adoption.

Objective

• To analyze how advancements in material science and transducer design have enhanced the accuracy, specificity, and field-deployability of modern soil sensors for key parameters like moisture, nutrients, pH, and temperature.
• To evaluate the extent to which integrated platforms such as Wireless Sensor Networks (WSN), the Internet of Things (IoT), and advanced spectroscopy can overcome the spatial and temporal limitations of conventional point-based soil sensing.
• To identify the primary technological and scalability challenges that must be addressed to transition advanced soil sensing technologies from research prototypes to robust, cost-effective, and widely adopted tools for sustainable soil management.

Study Configuration

• Spatial Scale: Point, plot, field, farm, regional, and global scales, ranging from localized in-situ measurements to extensive satellite-based monitoring.
• Temporal Scale: Real-time, continuous, periodic, on-demand, and long-term historical data archiving.

Methodology and Data

• Models used:
  • Machine learning algorithms: Artificial Neural Networks (ANNs), Random Forest, XGBoost, Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM).
  • Physical/Empirical models: Integral equation model (for backscatter), radiative transfer model, dielectric mixing model, agricultural water stress models, process-based models (for CO₂ leakage).
  • Geostatistical algorithms, digital twin frameworks.
• Data sources:
  • In-situ sensors: Tensiometers, gypsum blocks, thermal probes, neutron probes, dielectric sensors (Time Domain Reflectometry (TDR), Frequency Domain Reflectometry (FDR), Standing Wave Ratio (SWR), capacitance), Ion-Sensitive Field-Effect Transistors (ISFETs), optical pH sensors, Electrical Conductivity (EC) sensors, colorimetric/optical nutrient sensors, spectroscopy-based nutrient sensors (Visible (Vis), Ultraviolet (UV), Infrared (IR), X-ray Fluorescence (XRF), Inductively Coupled Plasma (ICP) spectroscopy, Laser-Induced Breakdown Spectroscopy (LIBS), Near-Infrared Reflectance Spectroscopy (NIRS)), Micro-Electro-Mechanical Systems (MEMS)-based sensors, polymer-based sensors, nanomaterial-based sensors, multi-parameter NPK sensors, thermocouples, thermistors, Resistance Temperature Detectors (RTDs), optical fiber-based temperature sensors, biosensors (microbial activity, aptamers, enzyme activity, nanoparticle-based), soil gas sensors (electrochemical, optical, Non-Dispersive Infrared (NDIR), catalytic, metal oxide).
  • Proximal sensing: Ground Penetrating Radar (GPR), multi-spectral and thermal imaging systems (Unmanned Aerial Vehicle (UAV)-based), Electromagnetic Induction (EMI) sensors, ultrasonic-based moisture sensors.
  • Remote sensing: Satellite-based optical (visible/near-infrared), thermal-infrared, microwave (active radar, passive radiometers), hyperspectral and multispectral imaging (drone/satellite).
  • Wireless Sensor Networks (WSNs), Internet of Things (IoT) platforms.
  • Geophysical measurements (apparent electrical conductivity), topographic variables from digital elevation, weather and yield data.

Main Results

• Advancements in microelectromechanical systems (MEMS), nanotechnology, and polymer-based sensors have significantly improved sensitivity and cost-effectiveness for rapid in-situ detection of NPK nutrients, reducing reliance on laboratory methods.
• Dielectric-based moisture sensors (TDR, FDR) and remote sensing techniques have proven highly effective for real-time irrigation management.
• Ion-sensitive field-effect transistors (ISFETs) and optical systems have enhanced nutrient profiling through improved pH and EC monitoring.
• The integration of these sensors into IoT-enabled networks facilitates extensive, real-time data collection and predictive analytics, supporting data-driven decision-making and overcoming spatial and temporal limitations of conventional point-based sensing.
• A multi-scale sensing paradigm, integrating in-situ, proximal (UAV), and remote (satellite) data with AI/ML data fusion, is essential for a holistic understanding of soil heterogeneity and predictive analytics.
• Key challenges persist, including the need for soil-specific calibration, high initial costs, limited connectivity in remote areas, sensor fouling, signal drift, and energy autonomy for wireless networks.
• Future prospects involve developing durable, cost-effective sensors with universal calibration models, leveraging machine learning for predictive analytics, employing biodegradable materials to enhance sustainability, and advancing Greenhouse Gas (GHG) and biological activity sensors.

Contributions

• Provides a comprehensive synthesis of recent advancements in sensor system technologies for soil health monitoring, outlining their transformative potential for precision agriculture.
• Systematically addresses three core research questions concerning sensor enhancement, the capacity of integrated platforms (WSN, IoT, spectroscopy) to overcome spatial and temporal limitations, and the technological and scalability challenges for widespread adoption.
• Details cutting-edge tools and sensor types for monitoring a wide range of soil parameters, including moisture, nutrients (NPK), pH, temperature, and biological activity.
• Identifies critical barriers to implementation, such as the need for soil-specific calibration, high costs, and connectivity issues, and proposes a roadmap for future research focusing on robustness, intelligence, interoperability, and accessibility.
• Emphasizes the crucial role of standardization, calibration, and multi-scale data integration (including AI/ML) in bridging the gap between innovative prototypes and practical, large-scale deployment in global agriculture.

Funding

No funding was received for conducting this study.

Citation

@article{Thingujam2025From,
  author = {Thingujam, Umalaxmi and Prabha, D. M. Mary Synthia Regis and Bag, Animesh Ghosh and Thingujam, Victor and Darshan, N. P. and Dutta, Suman and Gorain, Subrata},
  title = {From point sensing to intelligent systems: a comprehensive review on advanced sensor technologies for soil health monitoring},
  journal = {Discover Sensors},
  year = {2025},
  doi = {10.1007/s44397-025-00028-8},
  url = {https://doi.org/10.1007/s44397-025-00028-8}
}