Valcarce-Diñeiro et al. (2025) Artificial intelligence and Earth observation for agricultural applications

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-06
• Authors: Rubén Valcarce-Diñeiro, Oscar Ortega
• DOI: 10.1016/b978-0-443-40296-8.00020-3

Research Groups

• Centre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), University of Hull, Hull, United Kingdom
• Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom

Short Summary

This chapter reviews the application of artificial intelligence and Earth observation technologies to address challenges in agriculture, highlighting their potential to provide actionable insights and noting the superior accuracy of deep learning models.

Objective

• To synthesize how Earth observation and artificial intelligence can provide actionable insights for various agricultural applications, addressing critical challenges such as climate change, population growth, and food shortages.

Study Configuration

• Spatial Scale: Global to regional (review of diverse agricultural applications).
• Temporal Scale: Contemporary (addressing 21st-century challenges) and future-oriented (potential applications).

Methodology and Data

• Models used: Deep learning models (e.g., convolutional neural networks, recurrent neural networks), traditional machine learning algorithms (e.g., random forest, support vector machines, decision trees, gradient boosting).
• Data sources: Earth observation (e.g., satellite remote sensing).

Main Results

• Earth observation and artificial intelligence are powerful tools for generating actionable insights across various agricultural applications.
• Deep learning models generally achieve better accuracy compared to traditional machine learning algorithms in agricultural contexts.
• Key AI/ML algorithms frequently employed in agriculture include deep learning models (CNNs, RNNs) and traditional methods (Random Forest, SVMs, Decision Trees, Gradient Boosting).

Contributions

• Provides a comprehensive synthesis of the current state and potential of artificial intelligence and Earth observation in addressing contemporary agricultural challenges.
• Highlights the comparative performance of deep learning versus traditional machine learning algorithms in agricultural applications.
• Catalogs the main artificial intelligence and machine learning algorithms utilized in the field.

Funding

• Not specified in the provided text.

Citation

@article{ValcarceDiñeiro2025Artificial,
  author = {Valcarce-Diñeiro, Rubén and Ortega, Oscar},
  title = {Artificial intelligence and Earth observation for agricultural applications},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-40296-8.00020-3},
  url = {https://doi.org/10.1016/b978-0-443-40296-8.00020-3}
}