A high-throughput method for precise phenotyping sugarcane stalk mechanical strength using near-infrared spectroscopy

Abstract

Background: Sugarcane ( Saccharum officinarum L .) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Since there are no efficient methods for determining stalk mechanical strength in sugarcane, genetic approaches for improving stalk lodging resistance are largely limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time. Results: : Hundreds of sugarcane germplasms were harvested at the mature stage in the year of 2019 and 2020. In terms of determining rind penetrometer resistance (RPR) and breaking force, large variations of mechanical strength were found in the sugarcane stalk internodes, based on well-established laboratory measurements. Through partial least square regression analysis, two online NIRS models were established with a high coefficient of determination ( R 2 ) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R 2 and RPD values as high as 1.00 and 17.7, as well as showing relatively low root mean square error values at 0.44 N mm -2 during global modeling, demonstrating excellent predictive performance. Conclusions: : This study delivered a successful attempt for rapid and precise prediction of mechanical strength in sugarcane stalk by NIRS assay. By using these established models, genetic improvements could be made to phenotyping jobs for large-scale sugarcane germplasm.

Plant phenotyping relevance

サトウキビ茎の機械的強度という植物形質を、NIRSと回帰モデルで高スループット推定する手法を開発・検証しており、フェノタイピング手法が中心である。

Code and data availability

The preprint reports NIRS models for sugarcane stalk mechanical strength (RPR and breaking force) but provides no public repository, dataset deposit, or author code URL. Data availability only states data are in the article/supplementary files, which are not paper-specific public assets with an actionable URL among the

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