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 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.

Plant phenotyping relevance

FreezeNetは画像ベースでコムギの凍害形質を定量化する深層学習手法として開発・検証されており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The paper's data availability statement explicitly deposits the full FreezeNet implementation and trained model on the authors' public GitHub repository, which directly reproduces the paper's image-based freeze-injury phenotyping analysis. The 430 field images and trait tables are not stated as separately deposited (no