Efficient vegetation indices for phenotyping of abiotic stress tolerance in tea plant (Camellia sinensis (L.) Kuntze)

Abstract

Early non-destructive detection of stress effect is crucial for efficient breeding strategies and germplasm characterization. Recently developed hyperspectral technologies allow to perform fast real-time phenotyping through reflectance-based vegetation indices. However, efficiency of these vegetation indices has to be validated for each crop in different environment. The aim of this study was to reveal efficient vegetation indices for phenotyping of abiotic stress (cold, freezing and nitrogen deficiency) response in tea plant. Among 31 studied VIs, few indices were efficient to distinguish tolerant and susceptible tea plants under abiotic stress: ZMI (Zarco-Tejada & Miller Index), VREI1,2,3 (Vogelmann Red Edge Indices), RENDVI (Red Edge Normalized Difference Vegetation Index), CTR1 and CTR2 (Carter Indices). Most of these indices are calculated based on reflectance in near-infrared area at 705–760 nm, indicating this range as promising for tea germplasm characterization under abiotic stresses. Tolerant tea plants showed the following values under freezing: ZMI ≥1.90, VREI1 ≥ 1.40, RENDVI ≥0.38, Ctr1 ≤ 1.74. The leaf N-content was positively correlated (Pearson's) with the following indices ZMI, VREI1, RENDVI, while negatively correlated with CTR, and VREI2,3. These results will be useful for tea germplasm management, genomics and breeding research aimed at abiotic stress tolerance of tea plant.

Keywords

• Camellia sinensis
• Stress tolerance
• Phenotyping
• Vegetation indices
• Remote sensing
• Hyperspectral analysis