ArticlePlant methods2021
A method for obtaining field wheat freezing injury phenotype based on RGB camera and software control.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Relationships between freeze tolerance and plant architecture in winter wheat during tillering stage.Frontiers in plant science · 2026Article
- Monitoring and Risk Prediction of Low-Temperature Stress in Strawberries through Fusion of Multisource Phenotypic Spatial Variability Features.Plant phenomics (Washington, D.C.) · 2025Article
- FreezeNet: A Lightweight Model for Enhancing Freeze Tolerance Assessment and Genetic Analysis in Wheat.Plant phenomics (Washington, D.C.) · 2025Article
- DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery.Plant methods · 2025Article
- Accurate classification of wheat freeze injury severity from the color information in digital canopy images.PloS one · 2024Article
- RT-DETR-SoilCuc: detection method for cucumber germinationinsoil based environment.Frontiers in plant science · 2024Article
- YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor.Frontiers in plant science · 2023Article
- Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat.Plant methods · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundLow temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, a more efficient and accurate method for freezing damage identification is urgently needed.
resultsA high-throughput phenotyping system was developed in this paper, namely, RGB freezing injury system, to effectively and efficiently quantify the wheat freezing injury in the field environments. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. In this experiment, a total of 128 wheat varieties were planted, three nitrogen concentrations were applied and two biological and technical replicates were performed. And wheat canopy images were collected at the seedling pulling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. We compared different test parameters and found that the coverage had a greater impact on freezing injury. Therefore, we preliminarily divided four grades of freezing injury according to the test results to evaluate the freezing injury of different varieties of wheat at the seedling stage.
conclusionsThe automatic phenotypic analysis method of freezing injury provides an alternative solution for high-throughput freezing damage analysis of field crops and it can be used to quantify freezing stress and has guiding significance for accelerating the selection of wheat excellent frost resistance genotypes.
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