Ekatpure et al. (2025) Advancing Crop Cultivation Estimation with Aerial Imaging and Artificial Intelligence: A Comprehensive Review

Identification

• Journal: Lecture notes in networks and systems
• Year: 2025
• Date: 2025-11-13
• Authors: J. N. Ekatpure, Dinesh Bhagwan Hanchate
• DOI: 10.1007/978-3-032-06697-8_24

Research Groups

• Department of Computer Engineering, AISSMS College of Engineering, Savitribai Phule Pune University, Pune, India
• Department of Computer Engineering, Dattakala Faculty of Engineering, Savitribai Phule Pune University, Pune, India

Short Summary

This review paper comprehensively analyzes current methods and applications of Artificial Intelligence (AI) in crop cultivation estimation using aerial imagery, demonstrating its effectiveness in various agricultural fields for observation, yield prediction, and decision support.

Objective

• To provide a thorough review of current methods and applications of Artificial Intelligence in crop cultivation estimation using aerial images.
• To analyze the practical uses of AI techniques in agricultural functions to aid farmers, experts, and educators in understanding precision farming applications.

Study Configuration

• Spatial Scale: Global (as a comprehensive review covering various applications in "different agricultural fields").
• Temporal Scale: Current methods and recent developments in AI and aerial imaging for crop cultivation, with suggestions for future research.

Methodology and Data

• Models used: Artificial Intelligence techniques, including Machine Learning and Deep Learning (as indicated by the review's scope and keywords).
• Data sources: Aerial imagery (drone technology, high-resolution satellite imagery).

Main Results

• Artificial Intelligence techniques, when integrated with aerial imagery technology, can be accurately applied across diverse agricultural fields.
• These applications effectively enable advanced observation, accurate yield prediction, and provide crucial decision support for farmers.
• The review identifies current challenges and proposes future research directions to further enhance the application of AI in crop cultivation techniques.

Contributions

• Provides a comprehensive synthesis of the state-of-the-art in AI applications for crop cultivation estimation using aerial imaging.
• Highlights the practical utility and effectiveness of these technologies in real-world agricultural scenarios.
• Offers guidance for farmers, experts, and educators on leveraging AI in precision farming.
• Proposes future research avenues to overcome existing limitations and expand AI's role in agriculture.

Funding

• No specific funding projects or programs were mentioned in the provided paper text.

Citation

@article{Ekatpure2025Advancing,
  author = {Ekatpure, J. N. and Hanchate, Dinesh Bhagwan},
  title = {Advancing Crop Cultivation Estimation with Aerial Imaging and Artificial Intelligence: A Comprehensive Review},
  journal = {Lecture notes in networks and systems},
  year = {2025},
  doi = {10.1007/978-3-032-06697-8_24},
  url = {https://doi.org/10.1007/978-3-032-06697-8_24}
}