FreezeNet: A Lightweight Model for Enhancing Freeze Tolerance Assessment and Genetic Analysis in Wheat.

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

SUBMITTER: Sun F 

PROVIDER: S-EPMC12710041 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature