Predicting plant biomass accumulation from image-derived parameters

Abstract

Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species.

Plant phenotyping relevance

画像由来特徴量から植物バイオマスを推定する予測モデルを開発し、複数実験で性能評価しており、表現型取得・推定手法が研究の中心である。

Code and data availability

The paper's authors publicly deposited their analysis code (HTPmod R package on GitHub), raw image datasets in the IPK PGP repository under three DOIs, and supporting data (metadata, raw images, archival code copy) in the GigaScience GigaDB repository. All are paper-specific, public, and actionable.