A Strategy for the Acquisition and Analysis of Image-Based Phenome in Rice during the Whole Growth Period

Abstract

As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world's population, occupying an important position in China's agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice.

Plant phenotyping relevance

イネの全生育期間にわたる画像ベース形質の取得・解析戦略を開発しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper's Data Availability statement points to a public website hosting the related data and code (i-trait phenotype dataset and analysis code) for this rice image-based phenotyping study. The URL matches an allowed entry. Confidence is moderate because the statement uses future tense ('will be made available') and,