Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection

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.

Automated summary

This study establishes a model for estimating leaf chlorophyll content in drought-affected sugarcane using visible/near-infrared reflectance spectroscopy and characteristic band extraction techniques. The combination of characteristic bands extracted by the successive projection algorithm (SPA) with a Stacking regression model achieves a high chlorophyll content estimation performance with only 4.3% of original spectral variables as inputs.