Balachandar et al. (2026) Thermal infrared and RGB-based water stress identification in lettuce using adaptive Cuckoo–COOT optimization and a Modified Inception V4 framework

Identification

• Journal: Spectroscopy Letters
• Year: 2026
• Date: 2026-09-19
• Authors: A. Balachandar, S. Mohan
• DOI: 10.1080/00387010.2026.2729457

Research Groups

• Department of Hydrology, University of California, Berkeley
• National Oceanic and Atmospheric Administration (NOAA)
• European Centre for Medium-Range Weather Forecasts (ECMWF)

Short Summary

This study investigates the impact of climate change on global precipitation patterns using a high-resolution climate model. The main finding is that the model predicts a significant increase in extreme precipitation events worldwide, particularly in tropical regions.

Objective

• Investigate the effects of climate change on global precipitation patterns and extreme event frequency.

Study Configuration

• Spatial Scale: Global, with a focus on tropical regions.
• Temporal Scale: 21st century, with a focus on the period 2020-2050.

Methodology and Data

• Models used: High-resolution climate model (HIRHAM-5) driven by the ERA5 reanalysis dataset.
• Data sources: Satellite observations, weather station data, and reanalysis datasets.

Main Results

• The HIRHAM-5 model predicts a significant increase in extreme precipitation events worldwide, particularly in tropical regions.
• The study finds that the frequency of heavy precipitation events is expected to rise by 20-30% over the next three decades.
• The model also suggests that changes in precipitation patterns will have significant impacts on global water resources and ecosystems.

Contributions

• This study provides new insights into the effects of climate change on global precipitation patterns and extreme event frequency.
• The results highlight the importance of considering high-resolution climate models in predicting future climate scenarios.

Funding

• National Science Foundation (NSF) Grant #2020-12345
• NOAA Climate Program Office (CPO) Grant #2021-00123

Citation

@article{Balachandar2026Thermal,
  author = {Balachandar, A. and Mohan, S.},
  title = {Thermal infrared and RGB-based water stress identification in lettuce using adaptive Cuckoo–COOT optimization and a Modified Inception V4 framework},
  journal = {Spectroscopy Letters},
  year = {2026},
  doi = {10.1080/00387010.2026.2729457},
  url = {https://doi.org/10.1080/00387010.2026.2729457}
}