Linking Leaf Angle to Physiological Responses for Drought Stress Detection: Case Study on Quercus acutissima Carruth. in Forest Nursery

초록

Due to climate change, seedling damage caused by drought stress is expected to increase in both afforestation sites and nurseries. Therefore, to ensure stable seedling production under high-temperature conditions and to cultivate seedlings with enhanced drought tolerance through hardening treatments, the development of an effective irrigation system is required. Conventional physiological methods for non-destructive drought detection, such as chlorophyll fluorescence and leaf temperature measurements, require expensive and manual operation, thereby limiting their real-time applicability in forest nurseries. This study evaluated the applicability of using image-based leaf angle measurements for drought stress detection in Quercus acutissima Carruth. seedlings. One-year-old seedlings were grown under two water regimes-well-watered (CT: control) and unwatered (DT: drought)-through Day 8. Statistical analyses (RMANOVA) revealed that changes in the leaf angle parameter PMD-MD (the difference between the previous and current measurement days) showed treatment effects similar to those of the physiological responses Phi NO (quantum yield of non-regulated energy dissipation) and qL (fraction of open PSII reaction centers) to drought on Day 6. Leaf angle reflected drought stress but did not precede physiological changes, indicating its role as a complementary rather than an early indicator. Multiple regression models identified AT (air temperature), SM (soil moisture), Fm ' (maximum fluorescence in the light-adapted state), and VPD (vapor pressure deficit) as the main factors influencing leaf angle variation. Although leaf angle was affected by combined environmental stresses such as high temperature, it was less sensitive to heat stress than physiological responses based on RMANOVA results. These results indicate the potential of image-based leaf angle measurements for drought stress detection. To establish plant-based smart irrigation systems, future studies should validate and refine this approach using larger datasets.

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