부산대학교 도서관

The detection and characterization of physiological processes in crop plants under water-limited conditions is essential for the selection of drought-tolerant genotypes and the functional analysis of related genes. Close-range hyperspectral imaging (HSI) is a promising, non-invasive technique for sensing of plant traits, and has the potential to detect plant responses to water deficit stress at an early stage. The present study describes a data analysis method to realize this potential. Reflectance spectra of plants in close-range imaging are highly influenced by illumination effects. Standard normal variate (SNV) was applied to reduce linear illumination effects, while non-linear effects were filtered by discarding the affected pixels through a clustering procedure. Once the illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To quantify stress-related spectral dynamics, a spectral analysis procedure was developed based on a supervised band selection and a direct calculation of a spectral similarity measure against a reference. The proposed method was tested on HSI data of maize plants acquired in a high-throughput plant phenotyping platform for assessment of drought stress responses and recovery after re-watering events. Results show that the spectral analysis method successfully detected the drought stress responses at an early stage and consistently revealed the recovery effects shortly after the re-watering period.