Ali et al. (2025) Multi-sensors Remote Sensing and Machine Learning Techniques applications in Agriculture

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

Identification

• Journal: Egyptian Journal of Botany
• Year: 2025
• Date: 2025-12-15
• Authors: Abdelraouf M. Ali, Mohamed Aboelghar, Noha Morsy, Nazih Y. Rebouh, Dmitry E. Kucher, Hassan Hassan, Emad Abdeldaym, Nasser Saleh
• DOI: 10.21608/ejbo.2025.368976.3230

Research Groups

Not specified in the provided text, as this paper is a survey of existing literature rather than a report on new experimental research from specific groups.

Short Summary

This paper provides a comprehensive survey of the applications of advanced remote sensing techniques integrated with machine learning and other innovative technologies (IoT, AI, robotics) in modern agricultural practices, highlighting their advantages and limitations.

Objective

• To provide an updated survey informing researchers about recent techniques where remote sensing, combined with machine learning and other approaches, can be efficiently used in various agricultural applications, including a discussion of their advantages and limitations.

Study Configuration

• Spatial Scale: Global (review of diverse agricultural applications worldwide)
• Temporal Scale: Historical (past six decades for remote sensing evolution) to contemporary (recent techniques for ML/IoT/AI integration)

Methodology and Data

• Models used: Machine learning (ML), Internet of Things (IoT), Artificial Intelligence (AI), and robotics (as reviewed techniques applied in agriculture, not models used by the paper itself)
• Data sources: Remote sensing data (as data sources for the reviewed agricultural applications)

Main Results

• The integration of remote sensing with machine learning, IoT, AI, and robotics significantly expands its utility in agriculture beyond traditional monitoring to include modeling, prediction, and practical applications.
• The survey identifies numerous efficient applications of these combined techniques across various agricultural practices.
• The discussion includes a comprehensive overview of the advantages and limitations associated with these advanced tools in agriculture.

Contributions

• Provides an updated and comprehensive survey of the latest techniques combining remote sensing with machine learning and other advanced technologies (IoT, AI, robotics) for agricultural applications.
• Synthesizes the current state-of-the-art, offering researchers a consolidated view of recent advancements and their practical implications.
• Highlights both the benefits and constraints of these integrated approaches, guiding future research and implementation strategies.

Funding

Not specified in the provided text.

Citation

@article{Ali2025Multisensors,
  author = {Ali, Abdelraouf M. and Aboelghar, Mohamed and Morsy, Noha and Rebouh, Nazih Y. and Kucher, Dmitry E. and Hassan, Hassan and Abdeldaym, Emad and Saleh, Nasser},
  title = {Multi-sensors Remote Sensing and Machine Learning Techniques applications in Agriculture},
  journal = {Egyptian Journal of Botany},
  year = {2025},
  doi = {10.21608/ejbo.2025.368976.3230},
  url = {https://doi.org/10.21608/ejbo.2025.368976.3230}
}