Singh et al. (2026) Precision Agriculture with AI and IoT: Enhancing Farming Efficiency Through Technological Integration

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Pardeep Singh, Ayush Shandilya, Abhay Ghalot
• DOI: 10.1007/978-981-95-3701-3_29

Research Groups

• School of Computer Science and Engineering, Galgotias University, Greater Noida, India

Short Summary

This study investigates the integration of AI and IoT in precision agriculture, demonstrating the superior performance of the random forest model for irrigation forecasting to enhance water use efficiency and crop yields.

Objective

• To demonstrate the effectiveness and superiority of integrating AI (specifically the random forest model) and IoT in precision agriculture for enhancing farming efficiency, particularly in irrigation forecasting, to improve water use efficiency and crop yields.

Study Configuration

• Spatial Scale: Not explicitly defined, but implies agricultural fields or farm-level applications.
• Temporal Scale: Not explicitly defined, but implies real-time or near real-time operational decision-making for tasks like irrigation forecasting.

Methodology and Data

• Models used: Random Forest, Support Vector Machine (SVM) (for comparison).
• Data sources: Not explicitly stated, but implied to be data collected via IoT sensors for agricultural parameters, used for performance simulations and visualizations.

Main Results

• The random forest model demonstrated superior performance in simulations and visualizations for irrigation forecasting compared to models like SVM.
• Integration of AI and IoT leads to increased crop yields, reduced water wastage, improved resource utilization, and enhanced environmental sustainability.
• Advanced predictive models enable just-in-time operational decision-making, further increasing agricultural efficiency.

Contributions

• Provides a demonstration of the superior performance of the random forest model in AI-driven irrigation forecasting within an IoT framework for precision agriculture.
• Highlights the comprehensive benefits of AI and IoT integration in agriculture, including enhanced water use efficiency, increased crop yields, and improved environmental sustainability.
• Emphasizes the role of advanced predictive models in enabling real-time operational decision-making for agricultural efficiency.

Funding

• Not explicitly mentioned in the provided paper text.

Citation

@article{Singh2026Precision,
  author = {Singh, Pardeep and Shandilya, Ayush and Ghalot, Abhay},
  title = {Precision Agriculture with AI and IoT: Enhancing Farming Efficiency Through Technological Integration},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-981-95-3701-3_29},
  url = {https://doi.org/10.1007/978-981-95-3701-3_29}
}