Meryem et al. (2025) Review of AI methods in precision agriculture

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Springer Link (Chiba Institute of Technology)
• Year: 2025
• Date: 2025-12-19
• Authors: El Hou Meryem, Zahidi Yassine, Rabbah Nabila, El-Moufid Mohamed, Medromi Hicham, Touati Abdelwahed
• DOI: 10.1051/e3sconf/202568000077/pdf

Research Groups

Not specified in the provided text, as this is a review paper synthesizing work across various research groups and institutions involved in AI and agriculture.

Short Summary

This review analyzes the increasing adoption of Artificial Intelligence (AI) in precision agriculture, detailing how machine learning and deep learning technologies are advancing crop management, and concludes that AI can significantly boost agricultural resiliency, productivity, and sustainability despite existing challenges.

Objective

• To analyze the increasing adoption of Artificial Intelligence (AI) in precision agriculture, focusing on advancements in crop management brought by machine learning and deep learning technologies, and to identify associated challenges and future directions.

Study Configuration

• Spatial Scale: Global/General application context of precision agriculture.
• Temporal Scale: Current trends and recent advancements in AI adoption in agriculture.

Methodology and Data

• Models used: Machine Learning and Deep Learning technologies, including Support Vector Machines, Random Forest, Convolutional Neural Networks, Vision and Hybrid Transformers.
• Data sources: Literature review of existing scientific papers and applications in precision agriculture.

Main Results

• AI solutions are increasingly adopted in precision agriculture for tasks such as scouting, pest and disease detection, weeding, irrigation, and crop quality estimation, offering quicker, more precise, and scalable alternatives to manual work.
• Integration of AI with drones, sensors (Internet of Things), and robotics enables real-time monitoring, predictive analytics, and automated decision-making.
• AI is foreseen to enhance agriculture by reducing chemical use and improving overall efficiency.
• Significant challenges include high computational demands, limited availability of large high-quality datasets, high costs for smallholder farmers, and privacy concerns.
• Collaboration between AI specialists and agricultural scientists is crucial for developing affordable, reliable, and field-ready innovations to stimulate widespread adoption.

Contributions

• Provides a comprehensive overview of the increasing adoption and impact of AI, machine learning, and deep learning in precision agriculture.
• Highlights specific applications and technological integrations (drones, IoT, robotics) that are transforming crop management.
• Identifies key benefits of AI in enhancing agricultural resiliency, productivity, and sustainability.
• Outlines critical challenges hindering widespread AI adoption in agriculture and suggests interdisciplinary collaboration as a path forward.

Funding

Not specified in the provided text.

Citation

@article{Meryem2025Review,
  author = {Meryem, El Hou and Yassine, Zahidi and Nabila, Rabbah and Mohamed, El-Moufid and Hicham, Medromi and Abdelwahed, Touati},
  title = {Review of AI methods in precision agriculture},
  journal = {Springer Link (Chiba Institute of Technology)},
  year = {2025},
  doi = {10.1051/e3sconf/202568000077/pdf},
  url = {https://doi.org/10.1051/e3sconf/202568000077/pdf}
}