Fu Xiuqing, Bai Yang, Zhou Jing, Zhang Hongwen, Xian Jieyu
College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
Key Laboratory of Intelligence Agricultural Equipment of Jiangsu Province, Nanjing, 210031, China.
Plant Methods. 2021 Nov 26;17(1):120. doi: 10.1186/s13007-021-00821-7.
Low 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.
A 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.
The 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.
低温冻害对小麦幼苗生长和最终产量有不利影响。传统评估冻害对小麦造成损伤的方法是通过肉眼观察，这既耗时又费力。因此，迫切需要一种更高效、准确的冻害识别方法。
本文开发了一种高通量表型系统，即RGB冻害系统，以有效且高效地量化田间环境下的小麦冻害。该系统能够自动收集、处理和分析在田间条件下使用移动表型舱采集的小麦图像。还开发了一个数据管理系统，用于存储和管理系统中的原始图像和计算出的表型数据。在本实验中，共种植了128个小麦品种，施加了三种氮浓度，并进行了两次生物学和技术重复。在拔节期采集了小麦冠层图像，并为每个小麦样本提取了三个图像特征，包括ExG、ExR和ExV。我们比较了不同测试参数，发现覆盖率对冻害影响更大。因此，我们根据测试结果初步划分了四个冻害等级，以评估不同品种小麦在苗期的冻害情况。
冻害的自动表型分析方法为田间作物高通量冻害分析提供了一种替代解决方案，可用于量化冻害胁迫，对加速小麦优良抗冻基因型的筛选具有指导意义。
