Iyanda et al. (2025) Leveraging Artificial Intelligence and Iot for Precision Crop Management: Enhancing Physiological Responses in Stress-Prone Regions

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Identification

• Journal: NIPES Journal of Science and Technology Research
• Year: 2025
• Date: 2025-12-13
• Authors: Olumayowa J. Iyanda, Iretiayo Adelaiye, Olugbenga Agboola, Ayomide Afolabi, VICTORY OLUWASEYI ADELAIYE, Oluwadare Joshua Oyebode
• DOI: 10.37933/nipes/7.4.2025.si399

Research Groups

Not specified in the provided text.

Short Summary

This review examines the integration of Internet of Things (IoT) technologies with Artificial Intelligence (AI) models for precision agriculture, demonstrating how these systems can increase crop yields by 15–20% and enhance resource efficiency in stress-prone regions like Nigeria.

Objective

• To examine how Internet of Things (IoT) technologies integrated with Artificial Intelligence (AI) models can enable precise, real-time monitoring and adaptive management of crop systems to mitigate stress and improve yields.

Study Configuration

• Spatial Scale: Case studies conducted in Nigeria, focusing on arable crops like maize and tomatoes.
• Temporal Scale: Real-time monitoring over crop cycles.

Methodology and Data

• Models used: Artificial neural networks (for irrigation scheduling), Decision tree classifiers (for pest prediction).
• Data sources: Internet of Things (IoT) technologies including soil moisture sensors, weather stations, automated pest traps.

Main Results

• Application of AI-IoT systems resulted in a 15–20% increase in crop yields.
• Enhanced drought resistance in crops.
• More efficient use of water and nutrients.
• Reduced input costs for farmers.
• Improved environmental sustainability.

Contributions

• Synthesizes the benefits and integration strategies of IoT and AI for precision agriculture in stress-prone environments.
• Quantifies the potential impact with reported yield increases of 15–20% and improved resource efficiency.
• Identifies key areas for future research, including region-specific AI algorithms, field validation, and policy interventions to support adoption among smallholder farmers.

Funding

Not specified in the provided text.

Citation

@article{Iyanda2025Leveraging,
  author = {Iyanda, Olumayowa J. and Adelaiye, Iretiayo and Agboola, Olugbenga and Afolabi, Ayomide and ADELAIYE, VICTORY OLUWASEYI and Oyebode, Oluwadare Joshua},
  title = {Leveraging Artificial Intelligence and Iot for Precision Crop Management: Enhancing Physiological Responses in Stress-Prone Regions},
  journal = {NIPES Journal of Science and Technology Research},
  year = {2025},
  doi = {10.37933/nipes/7.4.2025.si399},
  url = {https://doi.org/10.37933/nipes/7.4.2025.si399}
}