Rao et al. (2025) IOT-Based Smart Precision Farming: A Comprehensive Review of Enabling Technologies

Identification

• Journal: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
• Year: 2025
• Date: 2025-11-26
• Authors: S. K. Rao, Javeed Akthar M, Ajay Kumar, P Khushi, K Nandini
• DOI: 10.55041/ijsrem54581

Research Groups

• Amruta Institute of Engineering and Management Sciences, Bidadi, Bangalore, India

Short Summary

This paper provides a comprehensive review of the core technologies and architectural frameworks essential for implementing robust Internet of Things (IoT)-based smart precision farming systems, aiming to enhance crop productivity, optimize resource usage, and promote sustainable agricultural practices.

Objective

• To review and synthesize the core technologies and architectural frameworks required for implementing effective IoT-based smart farming systems.

Study Configuration

• Spatial Scale: Review of systems applicable to various agricultural scales, from small-scale farms to larger fields, enabling site-specific optimization.
• Temporal Scale: Focuses on systems designed for real-time data acquisition, monitoring, and automated decision-making, with capabilities for historical data analysis.

Methodology and Data

• Models used: Rule-based systems, Machine Learning models (for predictive analytics like pest attack forecasting or optimal harvest times).
• Data sources: Real-time field data from Wireless Sensor Networks (WSNs) including soil moisture, air temperature, humidity, NPK levels, pH, light intensity; aerial imaging and monitoring from Unmanned Aerial Vehicles (UAVs); historical agricultural data for trend analysis.

Main Results

• Identified and detailed key hardware components for IoT-based smart farming, including various sensors (soil moisture, environmental, NPK, pH, light intensity), microcontrollers (ESP32, Arduino), communication modules (LoRaWAN, NB-IoT, Zigbee, Bluetooth), and actuators (solenoid valves, motorized pumps, dispensers).
• Outlined a layered system architecture comprising a perception layer, network layer, and application layer, leveraging cloud platforms (e.g., AWS IoT, Google Cloud IoT Core, ThingSpeak) for data storage, real-time monitoring, and advanced analytics.
• Emphasized the integration of decision-making algorithms, from simple rule-based systems to advanced machine learning models, for automated control of irrigation, fertilization, and pest management.
• Highlighted the importance of power management solutions, such as solar panels with rechargeable batteries, for continuous operation in remote agricultural settings.

Contributions

• Provides a comprehensive and structured review of the enabling technologies and architectural frameworks for IoT-based smart precision farming.
• Synthesizes information on hardware, communication protocols, data analytics, and decision-making processes, offering a holistic view of smart farming system implementation.
• Identifies the potential of these integrated systems to significantly increase crop productivity, optimize resource utilization (water, fertilizers), and foster sustainable agricultural practices.

Funding

• Not explicitly stated in the provided text. The authors acknowledge the faculty and staff of Amruta Institute of Engineering and Management Sciences for support and guidance.

Citation

@article{Rao2025IOTBased,
  author = {Rao, S. K. and M, Javeed Akthar and Kumar, Ajay and Khushi, P and Nandini, K},
  title = {IOT-Based Smart Precision Farming: A Comprehensive Review of Enabling Technologies},
  journal = {INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT},
  year = {2025},
  doi = {10.55041/ijsrem54581},
  url = {https://doi.org/10.55041/ijsrem54581}
}