Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
Source abstract
Abstract
To address the limitations of traditional manual measurements of phenotypes, a study focused on the rapid acquisition of phenotypic parameters in wheat grains was conducted. This research introduced an index for bud-point determination, which offers valuable insights into breeding selection, optimizing sowing and growth direction for grains, and assessing seed vitality. A grain collection device utilizing a linear array camera was designed and constructed, accompanied by custom software to simplify device operation. Image processing was performed using Halcon image, enabling the development and implementation of a system for extracting key phenotypic parameters. The preprocessing method involves isolating wheat grain regions, combined with S channel extraction, global thresholding, and opening and closing operations to achieve robust segmentation. Based on this preprocessing method, a phenotypic analysis was performed by evaluating the geometric characteristics of wheat grains. The process for extracting phenotypic characteristics and determining the bud point was subjected to experimental analysis. The results demonstrated high correlation and accuracy. The errors estimating the comprehensive grain length of five wheat varieties using the extraction algorithm developed in this study, the determination coefficient and root mean square error indices, were 0.986 and 0.0887, respectively, compared with manual measurements. Similarly, for grain width, the determination coefficient and RMSE were 0.9505 and 0.0541; for grain weight, these values were 0.7635 and 3.8329, respectively. Furthermore, the algorithm achieved a bud point direction determination accuracy rate of 97.5%. The mean error in the positioning accuracy of germination points was within 0.10 mm, and the mean angular error was maintained within ±3 degrees. The phenotype analysis of wheat using the image processing techniques presented in this study shows strong consistency with manual detection methods, providing valuable quantitative data to assist in breeding selection and accelerate genetic improvement practices.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spectroscopy and Chemometric Analyses · Smart Agriculture and AI · Genetic Mapping and Diversity in Plants and Animals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Funding
Key Laboratory Project of the Ministry of Education on Modern Agricultural Equipment and Technology · Youth Fund Project of the National Natural Science Foundation of China
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