A method for obtaining field wheat freezing injury phenotype based on RGB camera and software control

Abstract

Background 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. Results 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. Conclusions 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.

Temperature‐smart plants: A new horizon with omics‐driven plant breeding

“…Other studies have applied ML in breeding temperature-smart crops, particularly in freezing injury assessment (Wang et al, 2016;Cogato et al, 2020;Fu et al, 2021;Sanaeifar et al, 2023). For example, Li et al…”

“…They used motifs enriched near transcription start sites for thermal stress‐responsive genes to develop predictive models of gene expression responses, identifying known and novel cis‐regulatory elements involved in heat or cold stress and offering potential applications for designing stress‐responsive promoters using biotechnology approaches. Other studies have applied ML in breeding temperature‐smart crops, particularly in freezing injury assessment (Wang et al, 2016; Cogato et al, 2020; Fu et al, 2021; Sanaeifar et al, 2023). For example, Li et al (2022c) used five deep learning networks and unmanned aerial vehicle images to predict freezing‐tolerant rapeseed lines among more than 3,000 pure lines, reporting greater than 92% accuracy in recognizing freezing‐tolerant rapeseed.…”

YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor

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Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

“…The combination of climate change and the new crown epidemic has brought huge challenges to food security in China and the world [ 1 , 2 ]. The foundation for coping with challenges and ensuring national food security involves the analysis of the regulation mechanism of crop gene and phenotype formation, selection of new varieties with high yield, high quality, are green and stress resistance; realisation of precision cultivation and fine breeding methods and improvement of the utilisation efficiency of crop germplasm resources [ 3 , 4 ]. High-throughput crop-phenotype acquisition is the key to in-depth interpretation of gene functions and breaking through the bottleneck of precision breeding technology.…”