Sethi et al. (2026) Advancements in Smart Agriculture: Harnessing IoT and ML for Productivity, Sustainability and Food Safety

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Vandana Arora Sethi, Achintya Singhal
• DOI: 10.1007/978-3-032-14038-8_22

Research Groups

• Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India

Short Summary

This review paper provides a comprehensive analysis of the synergistic role of Internet of Things (IoT) and Machine Learning (ML) in smart agriculture, addressing an underexplored gap in the literature. It finds that integrating IoT sensor data with optimized ML models significantly improves accuracy, resource efficiency, and early warning systems across key agricultural domains.

Objective

• To provide a comprehensive analysis of how Machine Learning algorithms enhance the intelligence of IoT-based agricultural systems and their synergistic role in improving productivity, sustainability, and food safety.

Study Configuration

• Spatial Scale: Global review of applications across various agricultural domains.
• Temporal Scale: Synthesis of current methodologies and performance metrics from existing literature, highlighting challenges and future research directions.

Methodology and Data

• Models used: Machine Learning (ML) algorithms (e.g., for optimization, classification, prediction).
• Data sources: IoT sensor data (as reviewed from existing literature), encompassing various agricultural parameters.

Main Results

• The integration of IoT and ML significantly enhances data-driven, precision-based farming practices.
• IoT-ML integration was evaluated across five key areas: soil management, irrigation, crop monitoring and disease detection, livestock monitoring, and food safety.
• Combining sensor data with optimized ML models leads to significant improvements in accuracy, resource efficiency, and the effectiveness of early warning systems.
• Key challenges identified include data privacy, energy efficiency of systems, and system scalability for large-scale deployment.
• Future research directions emphasize developing robust, interoperable IoT frameworks, energy-harvesting sensor networks, and adaptive ML models for real-time, large-scale precision agriculture.

Contributions

• Provides a consolidated framework and comprehensive analysis of the synergistic role of IoT and ML in enhancing productivity, sustainability, and food safety in smart agriculture, addressing a previously underexplored gap.
• Synthesizes current methodologies and their performance metrics across diverse agricultural applications.
• Identifies critical challenges and proposes future research directions for the advancement of IoT-ML integration in agriculture.

Funding

• The authors received no support from any organizations for the submitted work.

Citation

@article{Sethi2026Advancements,
  author = {Sethi, Vandana Arora and Singhal, Achintya},
  title = {Advancements in Smart Agriculture: Harnessing IoT and ML for Productivity, Sustainability and Food Safety},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-3-032-14038-8_22},
  url = {https://doi.org/10.1007/978-3-032-14038-8_22}
}