Abstract

Drought is a major abiotic stress that affects the productivity of sugarcane worldwide. Water deficiency during sugarcane growth will lead to a reduction in leaf pigment content, such as chlorophyll, known as chlorosis. Although changes in spectral reflectance signature were identified a conspicuous sign of chlorophyll content changes caused by drought stress, the quantitative relationships between leaf chlorophyll content and spectral reflection signatures are still poorly explored. In this study, we present our contribution in systematically establishing a model for estimating leaf chlorophyll content in drought-affected sugarcane using VIS/NIR reflectance spectroscopy and characteristic band extraction techniques. Leaves of sugarcane plants at early elongation stage under different controlled irrigation conditions were used for spectra data collection, and the chlorophyll contents were collected with standard analytical methods. Different characteristic band extraction techniques and regression models were compared and discussed to obtain a chlorophyll content estimation model with the best performance. As the quantitative results, the combination of characteristic bands extracted by the successive projection algorithm (SPA) with a Stacking regression model achieved a high chlorophyll content estimation performance (R² = 0.9834, RMSE  = 0.0544 mg/cm²) with only 4.3% of original spectral variables as inputs. This study provides a theoretical basis for accurate and non-invasive drought stress level estimation in large-scale cultivation.

中文翻译：

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光谱测定甘蔗叶中叶绿素含量以检测干旱胁迫

摘要

干旱是影响全世界甘蔗生产力的主要非生物胁迫。甘蔗生长过程中缺水会导致叶绿素等叶子色素含量减少，称为失绿症。尽管光谱反射特征的变化被认为是干旱胁迫引起的叶绿素含量变化的显着标志，但叶片叶绿素含量和光谱反射特征之间的定量关系仍然很少被探索。在这项研究中，我们展示了我们在使用 VIS/NIR 反射光谱和特征带提取技术系统地建立估计受干旱影响的甘蔗叶片叶绿素含量的模型方面所做的贡献。采用不同控制灌溉条件下伸长初期的甘蔗植株叶片进行光谱数据采集，并采用标准分析方法采集叶绿素含量。对不同特征带提取技术和回归模型进行比较和讨论，得到性能最佳的叶绿素含量估算模型。从定量结果来看，通过逐次投影算法（SPA）提取的特征带与Stacking回归模型相结合，取得了较高的叶绿素含量估计性能（R ²  = 0.9834，RMSE   = 0.0544 mg/cm ²），仅4.3%原始光谱变量作为输入。该研究为大规模种植中准确、非侵入性干旱胁迫水平估算提供了理论依据。