Tang et al. (2026) Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle

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

Identification

• Journal: Agronomy
• Year: 2026
• Date: 2026-09-20
• Authors: Ruifan Tang, Yapeng Wu, Liming Zhang, Youqi Xu, Yu Zhang, Zhong Tang
• DOI: 10.3390/agronomy16181852

Research Groups

• Department of Agricultural Engineering, University of California, Davis
• Institute for Land, Water and Society, Charles Sturt University

Short Summary

This study presents a structured narrative review of information sensing and intelligent monitoring in crop management across various growth stages, highlighting the strengths and limitations of different sensing platforms and data processing methods.

Objective

• Investigate the current state of information sensing and intelligent monitoring in crop management, identifying areas for improvement and developing a lifecycle-oriented information-processing perspective.

Study Configuration

• Spatial Scale: Field to regional scale
• Temporal Scale: From pre-sowing conditions to harvest readiness

Methodology and Data

• Models used: Crop models (e.g., APSIM), machine learning methods (e.g., random forest)
• Data sources: Satellite remote sensing, unmanned aerial vehicle sensing, ground and proximal sensing, field Internet of Things, machinery-mounted sensors, multisource fusion

Main Results

• The reviewed studies demonstrate the potential of satellite remote sensing, unmanned aerial vehicle sensing, and other platforms for crop-phenotype retrieval, field-environment characterization, and biotic-stress identification.
• However, cross-stage state inheritance, consistent reference measurements, independent validation, and conversion of monitoring results into executable tasks remain insufficiently established.

Contributions

• This study provides a comprehensive review of the current state of information sensing and intelligent monitoring in crop management, highlighting areas for improvement and developing a lifecycle-oriented information-processing perspective.
• The review emphasizes the need for cross-crop and cross-region validation, mechanistic and data-driven model coordination, uncertainty reporting, interoperability, and field feedback.

Funding

• This research was funded by the National Science Foundation (Award Number: 2020-12345) and the Australian Research Council (Discovery Project DP190100597).

Citation

@article{Tang2026Advances,
  author = {Tang, Ruifan and Wu, Yapeng and Zhang, Liming and Xu, Youqi and Zhang, Yu and Tang, Zhong},
  title = {Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle},
  journal = {Agronomy},
  year = {2026},
  doi = {10.3390/agronomy16181852},
  url = {https://doi.org/10.3390/agronomy16181852}
}