Evaluation technologies for assessing drought tolerance of Kimchi cabbage seedlings using hyperspectral imaging and principal component analysis

Conventionally, methods for evaluating the tolerance of kimchi cabbage have primarily relied on visual inspection. The development of evaluation criteria based on hyperspectral imaging may help address the problem of decision-making errors arising from visual inspection. However, to evaluate the tolerance of kimchi cabbage using hyperspectral imaging, not only are hyperspectral big data processing and sophisticated exponential model development required, but also real-time evaluation technology with high field usability. This study presents an evaluation method for extracting vegetation information from hyperspectral data, logically analyzing the extracted data, validating the developed model, and ensuring field utility. The red-edge band between 680 and 700 nm is for evaluating the drought tolerance of Kimchi cabbage. The proposed method allows for assessing the drought tolerance of Kimchi cabbage seedlings within a range of 1 to 30, using a robust index with a variance of approximately 2.25 and a sensitivity index with a variance of around 8.4. Since the proposed nonlinear drought tolerance evaluation method must strongly correlate with the chlorophyll index, its performance was verified using the newly proposed cross-point analyzer. While nonlinear drought evaluation technology can serve as a robust analysis method based on extensive data, it has the disadvantage of requiring a long evaluation time. However, the additionally developed cross-point analyzer can improve the real-time capability of testing. Proper utilization of these two analytical technologies enhances the performance of robust and rapid drought tolerance evaluation for Kimchi cabbage seedlings. Therefore, this study introduces two techniques for non-destructively and quantitatively explaining plant water stress.