Supporting data for "Predicting plant biomass accumulation from image-derived parameters"

<br>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 ecologist. However, it is a great challenge to find a predictive biomass model across experiments.
<br>In the present study, we constructed four predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to three consecutive barley (<em>Hordeum vulgare</em>) 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 relieve the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high cross experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of plant biomass outcome. Furthermore, the 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.
<br>
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.

近年来，基于图像的高通量表型分析技术在植物科学领域快速发展，相较传统破坏性检测方法，该技术具备获取更多高价值生物学信息的巨大潜力。预测植物生物量是植物育种学家与生态学家的核心研究目标之一，然而构建可跨实验推广的生物量预测模型仍是一项巨大挑战。
本研究构建了四种预测模型，用以探究图像特征与植物生物量积累之间的定量关联。我们将所提出的分析方法应用于三项连续开展的大麦（*Hordeum vulgare*）实验，实验设置了对照组与胁迫处理组。结果证实，借助随机森林（Random Forest）模型，可基于图像参数精准预测植物生物量。该模型所实现的高精度预测能力，将有助于缓解育种应用中生物量检测环节的表型瓶颈问题。在环境条件相似的不同实验间，该模型仍可保持较高的预测性能。研究进一步量化了各单项特征对生物量预测的相对贡献度，为解析植物生物量积累的表型决定因子提供了全新认知。此外，本研究的分析方法还可用于筛选与植物生物量积累密切相关的关键图像特征，这为后续通过遗传作图解析生物量积累的遗传基础提供了可行方向。
本研究已成功构建可基于图像数据精准预测植物生物量积累的定量模型。我们预期，本研究的分析结果将有助于深化学界对植物生物量积累表型决定因子的认知，且所采用的统计方法可推广应用于其他植物物种的相关研究。