Revolutionizing rice breeding: High-throughput EOPT technology enhances panicle trait analysis

Supported by the Panicle Analyzer software, EOPT accurately measures key traits like grain number, grain length, grain width, and panicle length, eliminating the need for traditional manual threshing methods. This innovation promises to accelerate rice breeding programs, optimize phenotyping processes, and advance genetic research.

Rice is a staple crop for billions worldwide, making high-yield, resilient varieties critical for global food security. Panicle traits—such as grain size, number, and panicle length—are central to identifying germplasm quality and yield potential. Traditional methods to measure these traits are labor-intensive, prone to errors, and damage the grains, creating a bottleneck in breeding programs. Recent advancements in computer vision and artificial intelligence have opened new avenues for high-throughput phenotyping, enabling efficient and accurate trait detection.

A study (DOI: 10.34133/plantphenomics.0213) published in Plant Phenomics on 1st August 2024 by Wanneng Yang and Peng Song’s team from Huazhong Agricultural University, links specific genes, such as CTB4a and ES2, to grain size and panicle structure, offering valuable insights for breeding programs.

This study utilized the Extraction of Panicle Traits (EOPT) method to evaluate rice panicle phenotypes across diverse conditions. Researchers analyzed 5,554 images from 2,840 rice varieties in 2022 to construct and validate the grain counting model and assess its performance for measuring grain dimensions and panicle length. In 2023, additional images were captured from 45 varieties indoors and in the field to test the method’s adaptability to varying conditions. The grain counting model achieved remarkable accuracy, with prediction accuracy improving from 71.14% in traditional methods to 93.57% when utilizing the Panicle Morphology Index (PMI), significantly reducing the mean absolute error (MAE) and mean absolute percentage error (MAPE). For grain dimensions, EOPT demonstrated accuracies of 96.83% for length and 91.56% for width. Panicle length measurements achieved a robust accuracy of 97.13% under diverse imaging conditions. Field validation showed the model's adaptability, with high accuracies for grain number (93.68%), length (89.04%), and panicle length (89.46%), despite minor reductions compared to indoor results. To further enhance genetic insights, phenotypic data extracted through EOPT was used in a genome-wide association study (GWAS), identifying significant single-nucleotide polymorphisms (SNPs) related to grain size and panicle traits. Notable genes such as CTB4a, associated with cold tolerance and yield, were linked to these traits. These results validate EOPT's efficiency and reliability for rice phenotyping, providing a scalable and nondestructive approach to improve breeding strategies and genetic research in agriculture.

EOPT marks a paradigm shift in rice phenotyping, delivering precise, rapid, and nondestructive measurements of critical panicle traits. Its integration with genetic analysis tools provides a robust framework for understanding yield potential and enhancing breeding efficiency. This pioneering approach not only strengthens rice research but also holds promise for adaptation across other crops, driving global agricultural innovation.

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References

DOI

10.34133/plantphenomics.0213

Original Source URL

https://doi.org/10.34133/plantphenomics.0213

Funding information：

This work was supported by the National Key Research and Development Program of China (2021YFD1200504 and 2022YFD2002304), the Key Core Technology Project in Agriculture of Hubei Province (HBNYHXGG2023-9), and the Supporting Project for High-Quality Development of Seed Industry of Hubei Province (HBZY2023B001-06).

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.