ArticlePlant phenomics (Washington, D.C.)2025
FreezeNet: A Lightweight Model for Enhancing Freeze Tolerance Assessment and Genetic Analysis in Wheat.
Article in Plant phenomics (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Molecular mechanisms of plant freezing tolerance: from cold signal perception to adaptive responses.Frontiers in plant science · 2026Review
- Relationships between freeze tolerance and plant architecture in winter wheat during tillering stage.Frontiers in plant science · 2026Article
- RGB-Based Deep Learning for Freeze Damage Detection in Strawberry: Comparing Scratch and Transfer Learning Approaches on Custom Data.Plant direct · 2025Article
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Authors and funding
7 authors.
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Abstract
Freeze injury during the seedling stage significantly impacts wheat growth and yield, making the development of freeze-tolerant varieties crucial for ensuring stable yields. To identify key genetic factors for wheat freeze tolerance, an accurate assessment of freeze tolerance is necessary. However, traditional methods, such as visual inspection, are subjective and can vary significantly among observers. In this study, we developed FreezeNet, a lightweight deep learning model designed to accurately quantify freeze injury using an image-based phenotyping method. Freeze tolerance traits, including vegetation area (VA), green vegetation area (GVA), yellow vegetation fraction (YVF), and mean hue value (mHue), were extracted for freeze tolerance assessment. We captured standardized images with a smartphone and used FreezeNet to extract the freeze tolerance traits for 220 wheat accessions. These traits were strongly correlated with traditional injury scores estimated through visual inspection. Moreover, they presented relatively high heritability. Using these traits, we conducted genome-wide association studies (GWASs) to identify genetic loci associated with freeze tolerance. Eleven significant QTLs associated with freeze tolerance were identified, including 8 novel loci. By integrating four of these loci into a wheat germplasm that lacked any of the 11 QTLs, we significantly enhanced its freeze resistance, demonstrating the practical application of these genetic loci in breeding for improved freeze tolerance. Our results highlight FreezeNet as an advanced tool for assessing wheat freeze injury and identifying the genetic factors responsible for freeze tolerance, with the potential to guide breeding efforts toward the development of more resilient wheat varieties.
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